CHAPTER 4 RESULTS AND DISCUSSION

52
CHAPTER 4
RESULTS AND DISCUSSION
4.1 EVALUATION OF Aspergillus SPECIES USING RAPID
PLATE METHOD FOR L-ASPARAGINASE PRODUCTION
The rapid plate assay is advantageous as the method is quick and
L-asparaginase production can be visualized directly from the plates without
performing any time consuming assay (Gulati et al 1997). MCD media
supplemented with 0.009% of phenol red was used. Aspergillus manginii
(MTCC 1283), Aspergillus niger (MTCC 281), Aspergillus aculeatus (MTCC
1882), Aspergillus terreus (MTCC 1782), Aspergillus oryzae (MTCC 1847),
Aspergillus candidus (MTCC 1989), Aspergillus flavus (MTCC 2423),
Aspergillus foetidus (MTCC 508), Aspergillus awamori (MTCC 548) and
Aspergillus fumigatus (MTCC 870) were screened for L-asparaginase
production potential using rapid plate assay.
The diameter of appearance of pink colour (zone diameter) and
fungal colony diameter in MCD agar plates with and without phenol red were
observed after 48 h of incubation and reported in Table 4.1. The pink zone
formed in MCD plates with phenol red was directly correlated to the
L-asparaginase production potential of all Aspergillus sp. No pink colour was
formed in MCD plates without phenol red (control). The maximum zone
diameter of 38 mm was obtained for A. terreus MTCC 1782. A. terreus
MTCC 1782 was found to be good producer of L-asparaginase among
Aspergillus sp. reported in Table 4.1. Zone of diameter obtained for

53
Aspergillus terreus MTCC 1782 in rapid plates assay is shown in
Figure A.1.1: (a) plate with dye, (b) plate without dye and (c) plate with dye
and without inoculum. Rapid plate assay was found to be fast and an efficient
method for screening of fungal species. A. terreus MTCC 1782 was found to
be good producer of L-asparaginase among the Aspergillus species studied.
Thus A. terreus MTCC 1782 was selected as potential fungal source for
L-asparaginase production in further study.
Table 4.1 Zone and colony diameter of various Aspergillus species
screened for L-asparaginase production
Sl.
No.
Aspergillus
species
Zone
diameter,
mm
with dye
Colony
diameter,
mm
without dye
Zone
diameter,
mm
Colony
diameter,
mm
1 Aspergillus terreus 38 24 -- 22
2 Aspergillus niger 36 22 -- 20
3 Aspergillus oryzae 34 20 -- 22
4
Aspergillus
fumigates
34 22 -- 20
5 Aspergillus flavus 32 20 -- 18
6
7
8
9
10
Aspergillus
awamori
Aspergillus
aculeatus
Aspergillus
foetidus
Aspergillus
candidus
Aspergillus
manginii
28 18 -- 22
24 16 -- 18
18 10 -- 12
20 12 -- 14
16 12 -- 14

54
4.2 EVALUATION AND OPTIMIZATION OF VARIOUS
NATURAL SUBSTRATE, SODIUM NITRATE AND
L-ASPARAGINE FOR L-ASPARAGINASE PRODUCTION
BY Aspergillus terreus MTCC 1782 USING CLASSICAL
METHOD OF ONE-FACTOR AT A TIME APPROACH
The effect of synthetic L-proline and natural substrates namely
SBMF, GNOC powder, CSOC powder, peanut flour and wheat bran powder
in submerged fermentation of A. terreus MTCC 1782 for production of
extracellular L-asparaginase was studied using classical method of one factor
at a time approach. The influence of sodium nitrate as additional nitrogen
source, L-asparagine as inducer was also studied.
4.2.1 Effect of synthetic L-proline and natural substrates on
L-asparaginase production
The concentration of L-proline was varied from 0.5% to 3% (w/v)
in MCD media to study the effect of L-proline on L-asparaginase production
by A. terreus MTCC 1782. The operating condition variables such as
agitation speed, temperature and pH were maintained at 160 rpm, 32ºC and
6.2, respectively (Sarquis et al 2004). L-asparaginase activity obtained in
different days for varied concentration of L-proline is shown in Figure 4.1.
The MCD media containing 2% L-proline showed maximum enzyme activity
of 13.92 IU/mL on second day. Decrease in L-asparaginase activity was
observed after second day for all L-proline concentration.
The SBMF, GNOC powder, CSOC powder, peanut flour and wheat
bran powder (mesh size of 80/120) in MCD media were evaluated as alternate
to L-proline for L-asparaginase production by A. terreus MTCC 1782. The
operating conditions such as agitation speed, temperature and pH were
maintained at 160 rpm, 32ºC and 6.2. The concentration of soya bean meal
was varied from 1% to 5% in MCD media. The effect of varied concentration

55
of soya bean meal on L-asparaginase production is shown in Figure 4.2. It was
observed that the MCD media containing 3% soya bean meal showed
maximum enzyme activity of 9 IU/mL on third day of growth, while other
concentrations exhibited lower enzyme activity.
The concentration of GNOC powder was varied from 1% to 5% in
MCD media. The effect of varied concentration of GNOC powder on
L-asparaginase production is shown in Figure 4.3. It was observed that media
containing 2% GNOC powder showed maximum enzyme activity of
9.17 IU/mL on fourth day, while other concentrations exhibited lower enzyme
activity. The effect of varied concentration of cottonseed oil cake on
L-asparaginase production is shown in Figure 4.4. It was observed that the
media containing 3% concentration of CSOC powder showed maximum
enzyme activity of 8.26 IU/mL on fourth day, while other concentrations
exhibited lower enzyme activity.
The concentration of peanut flour was varied from 1% to 5 % in
MCD media. The effect of varied concentration of peanut flour on
L-asparaginase production is shown in Figure 4.5. It was observed that media
containing 4% peanut flour showed maximum enzyme activity of 7.25 IU/mL
on third day, while other concentration exhibited lower enzyme activity. The
concentration of wheat bran powder was varied from 1% to 5% in MCD
media. The effect of varied concentration of wheat bran powder on
L-asparaginase production is shown in Figure 4.6. It was observed that media
containing 2% wheat bran showed maximum enzyme activity of 4.32 IU/mL
on fourth day, while other concentrations exhibited lower enzyme activity.
The decrease in enzyme activity was observed after 3 rd /4 th day of
incubation. This may be due to the depletion of the substrate at all concentrations.
However the increase in L-asparaginase activity is found with increase in substrate
concentration and then the activity decreases at their higher concentrations. The
decrease in enzyme activity at higher concentration of substrate may be due to

56
the substrate inhibition. The optimum level of these substrates was selected to
explore the effect of additional nitrogen source (Sodium nitrate) and inducer
(L-asparagine).
Figure 4.1 Effect of synthetic L-proline on L-asparaginase production
Figure 4.2 Effect of SBMF on L-asparaginase production

57
Figure 4.3 Effect of GNOC powder on L-asparaginase production
Figure 4.4 Effect of CSOC powder on L-asparaginase production

58
Figure 4.5 Effect of peanut flour on L-asparaginase production
Figure 4.6 Effect of wheat bran powder on L-asparaginase production

59
4.2.2 Effect of sodium nitrate on L-asparaginase production using
synthetic L-proline and natural substrates
The effect of sodium nitrate as supplementary nitrogen source on
L-asparaginase production by A. terreus MTCC 1782 using optimum
concentration of synthetic L-proline, SBMF, GNOC powder, CSOC powder,
peanut and wheat in MCD media was studied. Sodium nitrate concentration
was varied from 0.5% to 2.5%. The effect of varied concentration of sodium
nitrate on L-asparaginase production using 2% L-proline in MCD media is
shown in Figure 4.7. It was observed that the 1.5% of sodium nitrate in MCD
media containing 2% L-proline has shown maximum L-asparaginase activity
of 23.31 IU/mL on third day, while other sodium nitrate concentration
exhibited lower L-asparaginase activity.
The effect of varied concentration of sodium nitrate on
L-asparaginase production using MCD media containing 3% SBMF media is
shown in Figure 4.8. It was observed that the media containing 2% sodium
nitrate showed maximum L-asparaginase activity of 16.08 IU/mL on third day
while other sodium nitrate concentration exhibited lower L-asparaginase
activity.
The effect of varied concentration of sodium nitrate on
L-asparaginase production using 2% GNOC powder in MCD media is shown
in Figure 4.9. It was observed that the media containing 1% sodium nitrate
showed maximum L-asparaginase activity of 10.83 IU/mL on forth day, while
other sodium nitrate concentrations exhibited lower L-asparaginase activity.
The effect of varied concentration of sodium nitrate on L-asparaginase
production using 3% cottonseed oil cake in MCD media is shown in
Figure 4.10. The highest L-asparaginase activity is obtained for 1% sodium
nitrate, with 10.61 IU/mL on fifth day, while other sodium nitrate
concentration exhibited lower L-asparaginase activity.

60
The effect of varied concentration of sodium nitrate on
L-asparaginase production using MCD media containing 4% peanut media is
shown in Figure 4.11. It was observed that the media containing 2% sodium
nitrate showed maximum L-asparaginase activity of 13.60 IU/mL on third day
while other sodium nitrate concentration exhibited lower L-asparaginase
activity. The effect of varied concentration of sodium nitrate on
L-asparaginase production using MCD media with 2% wheat bran powder is
shown in Figure 4.12. It was observed that the media containing 2% sodium
nitrate showed maximum L-asparaginase activity of 7.41 IU/mL on fourth day
while other sodium nitrate concentrations exhibited lower L-asparaginase
activity.
The 0.5% sodium nitrate has not significantly increased the
L-asparaginase activity after fourth day of fermentation; this may be due to
the insufficient nitrogen source. The 1% sodium nitrate was identified to give
maximum L-asparaginase production using synthetic L-proline. The 2%
sodium nitrate was identified to yield maximum L-asparaginase activity for
the natural substrates, namely, SBMF, peanut flour and wheat bran powder.
The 1% sodium nitrate was identified to give maximum L-asparaginase
production using natural substrates, namely, GNOC and CSOC. The optimum
concentration of sodium nitrate was used for further investigation on effect of
L-asparagine in L-asparaginase production by A. terreus MTCC 1782.

61
Figure 4.7
Effect of sodium nitrate on L-asparaginase production using
2% synthetic L-proline
Figure 4.8
Effect of sodium nitrate on L-asparaginase production using
3% SBMF

62
Figure 4.9
Effect of sodium nitrate on L-asparaginase production using
2% GNOC powder
Figure 4.10 Effect of sodium nitrate on L-asparaginase production using
3% CSOC powder

63
Figure 4.11 Effect of sodium nitrate on L-asparaginase production using
4% peanut flour
Figure 4.12 Effect of sodium nitrate on L-asparaginase production using
2% wheat bran powder

64
4.2.3 Effect of L-asparagine on L-asparaginase production using
synthetic L-proline and natural substrates
The L-asparagine concentration was varied from 0.4% to 1.4% in
MCD to study its effect on L-asparaginase production by A. terreus MTCC
1782 using optimum concentration of substrates namely synthetic L-proline,
SBMF, GNOC powder, CSOC powder, wheat bran powder, peanut powder
and supplemented with optimum concentration of sodium nitrate as additional
nitrogen source. L-asparagine acts as an inducer in L-asparaginase production.
The effect of L-asparagine on production of L-asparaginase using 2%
L-proline supplemented and 1% sodium nitrate in MCD media is shown in
Figure 4.13. It was observed that the 1% L-asparagine gave maximum
L-asparaginase activity of 34.98 IU/mL on the third day of fermentation,
while other L-asparagine concentrations exhibited low L-asparaginase activity.
The MCD media with optimal concentration of the natural substrate
and sodium nitrate was used to study the effect of L-asparagine on
L-asparaginase production by A. terreus MTCC 1782. L-asparagine acts as a
precursor in L-asparaginase production. The L-asparagine was varied from
0.6% to 1.4%. The effect of L-asparagine on L-asparaginase production using
3% SBMF and 2% sodium nitrate MCD media is shown in Figure 4.14. The
1.2% L-asparagine has shown maximum L-asparaginase activity of 27.78 IU/mL
on third day of fermentation, while the other L-asparagine concentrations
exhibited low L-asparaginase activity. The effect of L-asparagine on
L-asparaginase production using 2% GNOC powder and 1% of sodium nitrate
in MCD media is shown in Figure 4.15. It was observed that the 1.2%
L-asparagine gave maximum L-asparaginase activity of 30.35 IU/mL on
fourth day of fermentation, while other L-asparagine concentrations exhibited
lower enzyme activity.
The effect of L-asparagine on L-asparaginase production using 3%
CSOC powder and 1% sodium nitrate in MCD media is shown in Figure 4.16.

65
It was observed that the 1% L-asparagine gave maximum L-asparaginase
activity of 22.82 IU/mL on fifth day of fermentation, while other L-asparagine
concentrations exhibited low L-asparaginase activity. The effect of
L-asparagine on L-asparaginase production using MCD media with 4% peanut
flour and 2% sodium nitrate is shown in Figure 4.17. The 1.2% L-asparagine
has shown maximum L-asparaginase activity of 15.20 IU/mL on third day of
fermentation, while the other L-asparagine concentrations exhibited low
L-asparaginase activity. The effect of L-asparagine on MCD media with 2%
wheat bran powder and 2% sodium nitrate is shown in Figure 4.18. The 1.2%
L-asparagine has shown maximum L-asparaginase activity of 10.03 IU/mL on
fourth day of fermentation, while the other L-asparagine concentrations exhibited
low L-asparaginase activity. The decrease in L-asparaginase activity with increase
in L-asparagine above 1.2% optimum concentration was observed. The
L-asparaginase production by A. terreus MTCC 1782 was found enhanced
using the optimum concentration of all substrate along with sodium nitrate as
supplementary nitrogen source and L-asparagine as an inducer MCD media.
Figure 4.13 Effect of L-asparagine on L-asparaginase production using
2% L-proline and 1% sodium nitrate

66
Figure 4.14 Effect of L-asparagine on L-asparaginase production using
3% SBMF and 2% sodium nitrate
Figure 4.15 Effect of L-asparagine on L-asparaginase production using
2% GNOC powder and 1% sodium nitrate

67
Figure 4.16 Effect of L-asparagine on L-asparaginase production using
3% CSOC powder and 1% sodium nitrate
Figure 4.17 Effect of L-asparagine on L-asparaginase production using
4% peanut flour and 2% sodium nitrate

68
Figure 4.18 Effect of L-asparagine on L-asparaginase production using
2% wheat bran powder and 2% sodium nitrate
The maximum
L-asparaginase activity obtained for optimum
concentration of various substrates, sodium nitrate and L-asparagine are
compared with control experiment using L-asparagine as sole nitrogen source.
The MCD media with 1% L-asparagine as the sole nitrogen source (control
experiment) gave the L-asparaginase activity of 5.71 IU/mL. The maximum
L-asparaginase activity of 34.98 IU/mL was obtained using MCD media with
2% L-proline, 1% sodium nitrate and 1% L-asparagine. The maximum
L-asparaginase activity of 30.35 IU/mL was obtained using MCD media with
2% GNOC powder, 1% sodium nitrate and 1.2% L-asparagine. The maximum
L-asparaginase activity of 27.78 IU/mL was obtained using MCD media with
3% SBMF, 2% sodium nitrate and 1.2% L-asparagine. The maximum
L-asparaginase activity obtained using CSOC powder (22.82 IU/mL), peanut
flour (15.19 IU/mL) and wheat bran powder (10.03 IU/mL) based MCD
media supplemented with sodium nitrate and L-asparagine was comparatively

69
lower than L-proline, GNOC powder and SBMF based MCD media
supplemented with sodium nitrate and L-asparagine.
The L-asparaginase activity obtained using optimum concentration
of various substrate, sodium nitrate as supplementary nitrogen source and
L-asparagine as an inducer MCD media was comparatively higher than the
maximum L-asparaginase activity of 19.5 IU/mL obtained using media
containing 2% (w/v) L-asparagine as the sole substrate along with 1% (w/v)
ammonium sulfate as an additional nitrogen source using isolated Aspergillus sp.
(Sreenivasulu et al 2009) and L-asparaginase activity of 6.3 IU/mL for
Bipolaris sp. BR438 using MCD medium containing 1% L-asparagine as the
sole substrate (Lapmak et al 2010).
Thus the synthetic L-proline, SBMF and GNOC powder based
MCD media with sodium nitrate and L-asparagine were considered for
optimization of carbon source and operating conditions for L-asparaginase
production by A. terreus MTCC 1782 using classical method of one-factor at
a time approach.
4.3 EVALUATION AND OPTIMIZATION OF CARBON
SOURCE FOR L-ASPARAGINASE PRODUCTION BY
A. terreus MTCC 1782 USING CLASSICAL METHOD OF
ONE FACTOR AT A TIME APPROACH
The independent effect of various carbon sources such as glucose,
sucrose, maltose, fructose and lactose on L-asparaginase production was
studied using optimum concentration of different substrates namely, synthetic
L-proline, SBMF and GNOC powder in MCD media. The concentration of
sodium nitrate and L-asparagine was fixed at their optimum concentration
found in classical method. The best carbon source was optimized using
classical method of one factor at a time approach.

70
The influence of various carbon sources such as glucose, sucrose,
maltose, fructose and lactose was studied for extracellular L-asparaginase
production by A. terreus MTCC 1782 in submerged fermentation using
synthetic L-proline, SBMF and GNOC powder. The effect of various carbon
sources on L-asparaginase production by A. terreus MTCC 1782 is shown in
Figure 4.19, 4.20 and 4.21. The glucose was found as the best carbon source
for L-asparaginase production by A. terreus MTCC 1782 using MCD media
with L-proline as substrate along with 1% sodium nitrate and 1% L-asparagine
(Figure 4.19). The maximum L-asparaginase activity of 30.50 IU/mL
observed was for glucose followed by fructose and lactose. Sucrose was
found as the best carbon source for L-asparaginase production by A. terreus
MTCC 1782 using MCD media with 2% GNOC powder as substrate along
with 1% sodium nitrate and 1.2% L-asparagine (Figure 4.20). The maximum
L-asparaginase activity observed was 30.39 IU/mL for glucose followed by
fructose and lactose. The glucose was found as the best carbon source for
L-asparaginase production by A. terreus MTCC 1782 using MCD media with
3% SBMF as substrate with 2% sodium nitrate and 1.2% L-asparagine
(Figure 4.21). The maximum L-asparaginase activity observed was 29.86
IU/mL for glucose followed by fructose and lactose.
The effect of varied concentration of glucose on L-asparaginase
production by A. terreus MTCC 1782 using 2% L-proline as substrate, 1%
sodium nitrate and 1% L-asparagine is shown in Figure 4.22. The glucose
concentration was from 0.2% to 1.2%. It was observed that the 0.6% glucose
gave the maximum L-asparaginase activity of 35.03 IU/mL; low
L-asparaginase activity was observed for all other glucose concentration. The
effect of varied concentration of sucrose on L-asparaginase production by
A. terreus MTCC 1782 using MCD media with 2% groundnut oil cake flour
as substrate, 1% sodium nitrate and 1.2% L-asparagine is given in shown in

71
Figure 4.23. The sucrose concentration was from 0.2% to 1.2%. It was
observed that the 0.8% sucrose gave the maximum L-asparaginase activity of
35.30 IU/mL; low L-asparaginase activity was observed for all other sucrose
concentration. The effect of glucose on L-asparaginase production by
A. terreus MTCC 1782 using MCD media with 3% SBMF as substrate, 2%
sodium nitrate and 1.2% L-asparagine is shown in Figure 4.24. The glucose
concentration was from 0.2% to 1.2%. It was observed that the 0.6% glucose
gave the maximum L-asparaginase activity of 36.04 IU/mL; low
L-asparaginase activity was obtained at all other glucose concentration. Hence
the 0.6% glucose, 0.8% sucrose and 0.6% glucose were for further studies in
L-asparaginase production using L-proline, GNOC powder and SBMF,
respectively.
The role of carbon source in the synthesis of L-asparaginase is
controversial. It is generally accepted as catabolic repression in the case of
E. coli and Erwinia aeroideae at higher concentration (Jeffries 1976; Liu et al
1972). Glucose was the best carbon source under aerobic conditions for
synthesis of L-asparaginase by Serratia marcescens (Sukumaran et al 1979).
In contrast when the gram-positive bacteria were grown on nutritional
conditions in which the nitrogen source was apparently the limiting factor for
growth, the level of L-asparaginase activity increased, whereas the
L-asparaginase activity was decreased when limiting for carbon source
(Golden et al 1985). In the present work the MCD media containing rich
nitrogen source along with carbon source enhanced the L-asparaginase
production by A. terreus MTCC 1782.

72
Figure 4.19 Effect of various carbon sources on L-asparaginase
production using 2% L-proline, 1% sodium nitrate and 1%
L-asparagine
Figure 4.20 Effect of various carbon sources on L-asparaginase
production using 2% GNOC powder, 1% sodium nitrate
and 1.2% L-asparagine

73
Figure 4.21 Effect of various carbon sources on L-asparaginase
production using 3% SBMF, 2% sodium nitrate and 1.2%
L-asparagine
Figure 4.22 Effect of glucose on L-asparaginase production using 2%
L-proline, 1% sodium nitrate and 1% L-asparagine

74
Figure 4.23 Effect of sucrose on L-asparaginase production using 2%
GNOC powder, 1% sodium nitrate and 1.2% L-asparagine
Figure 4.24 Effect of glucose on L-asparaginase production using 3%
SBMF, 2% sodium nitrate and 1.2% L-asparagine

75
4.4 STATISTICAL AND EVOLUTIONARY OPTIMIZATION
OF FERMENTATION MEDIA COMPONENTS FOR
ENHANCED L-ASPARAGINASE PRODUCTION BY
A. terreus MTCC 1782
The sequential optimization strategy of design of experiments and
ANN linked GA were employed for evaluation and optimization of
fermentation media components for enhanced production of L-asparaginase
by A. terreus 1782 under submerged fermentation using different substrates
namely synthetic L-proline, SBMF and GNOC powder.
4.4.1 Optimization of media components for L-asparaginase
production using L-proline as substrate
The independent effect of fermentation media components for
L-asparaginase production by A. terreus MTCC 1782 using MCD media
containing synthetic substrate L-proline was evaluated using PB design. The
first four significant and important fermentation media components were
further optimized using RSM based 5-level CCD for four variables and ANN
linked GA for maximum L-asparaginase production.
4.4.1.1 Sequential optimization of media components using design of
experiments
The effect of seven medium components and initial pH on
production of L-asparaginase by A. terreus was studied using PB design given
in Table 4.2. The L-asparaginase activity obtained using PBD experiments
showed a wide variation from 10.88 IU/mL to 36.58 IU/mL of L-asparaginase
activity. It was subjected to statistical analysis using MINITAB 15.0 software
to estimate independent effect, t-value, p-value and confidence level. The
regression coefficients, t-value and confidence level are reported in Table 4.3.

76
The variables with positive regression coefficient represent an increase in
L-asparaginase activity due to the increase in concentration of the variables.
The variables with negative regression coefficient represent decrease in
L-asparaginase activity due to the increase in concentration of the variables.
The media components namely L-proline (x 1 ), sodium nitrate (x 2 ),
L-asparagine (x 3 ), glucose (x 4 ) and pH (x 8 ) were found to increase the
L-asparaginase activity at their high level. Whereas, the media components
namely di-potassium hydrogen phosphate (x 5 ), magnesium sulfate (x 6 ) and
potassium chloride (x 7 ) were found to decrease the L-asparaginase activity at
their higher level.
Table 4.2 PBD in actual unit of L-proline (x 1 ), sodium nitrate (x 2 ),
L-asparagine (x 3 ) and glucose (x 4 ), di-potassium hydrogen
phosphate (x 5 ), magnesium sulfate (x 6 ), potassium chloride
(x 7 ) and pH (x 8 ) with experimental L-asparaginase activity
Std.
Order
Media components, % (w/v)
x 1 x 2 x 3 x 4 x 5 x 6 x 7 x 8
L-Asparaginase
activity,
IU/mL
1 3.0 0.5 1.5 0.5 0.1 0.04 0.06 6.5 28.37
2 1.0 1.5 0.5 1.0 0.1 0.04 0.04 6.5 36.58
3 1.0 1.5 1.5 0.5 0.2 0.04 0.04 6.0 26.55
4 3.0 0.5 1.5 1.0 0.1 0.06 0.04 6.0 32.69
5 3.0 1.5 0.5 1.0 0.2 0.04 0.06 6.0 22.50
6 3.0 1.5 1.5 0.5 0.2 0.06 0.04 6.5 34.87
7 1.0 1.5 1.5 1.0 0.1 0.06 0.06 6.0 34.29
8 1.0 0.5 1.5 1.0 0.2 0.04 0.06 6.5 22.93
9 1.0 0.5 0.5 1.0 0.2 0.06 0.04 6.5 16.32
10 3.0 0.5 0.5 0.5 0.2 0.06 0.06 6.0 12.16
11 1.0 1.5 0.5 0.5 0.1 0.06 0.06 6.5 10.88
12 1.0 0.5 0.5 0.5 0.1 0.04 0.04 6.0 10.93

77
Table 4.3 Statistical analysis of PBD for evaluation of media components
and initial pH for L-asparaginase production
Variable
Main
effect
Coefficients t-value p-value
Intercept 24.089 20.14

78
Figure 4.25 Pareto plot shows the effect of media components and
operating conditions on L-asparaginase activity (X 1 - L-proline;
X 2 - Sodium nitrate; X 3 -L-asparagine; X 4 - Glucose; X 5 - Dipotassium
hydrogen phosphate; X 6 - Magnesium sulfate;
X 7 - Potassium chloride; X 8 - initial pH)
The central composite design with two axial points at a distance
α=2 from the design center and six replicates about the center point, making a
total of 31 runs given in Table 4.4, was used for optimization of significant
fermentation media components. The concentration of other media
components was kept constant. The central composite design experiment was
designed using the MINITAB 14.0 software. L-asparaginase production by
A. terreus was carried out in 50 ml of submerged batch cultures in 250 mL
Erlenmeyer flasks. The student’s t-test and F-test were performed using
MINITAB 15.0 software for experimental L-asparaginase activity reported in
Table 4.4. The coefficients, t-value and p-value for linear, quadratic and
combined effects were given in the Table 4.5 at 95% significance level
(p≤0.05). The p-values are used as a tool to check the significance of each of
the coefficients, which in turn may indicate the pattern of the interactions
between the variable. The smaller the p-value more significant is the

79
corresponding coefficient. It was observed that the coefficient for overall
effect of the variables was highly significant (p

80
where Y LA is the L-asparaginase activity (IU/mL), X i and X j are independent
variables (media components) in coded units. The RSM regression model
predicated L-asparaginase activity for each CCD experiment is presented in
Table 4.4.
Table 4.4 Five-level CCD in actual unit of media components with
Std.
Order
experimental, RSM and ANN predicted L-asparaginase activity
Media components, % (w/v) L-Asparaginase activity, IU/mL
RSM ANN
x 1 x 2 x 3 x 4 Experimental
predicted predicted
1 1.0 0.5 0.5 0.5 18.34 15.90 18.33
2 3.0 0.5 0.5 0.5 23.09 23.05 23.21
3 1.0 1.5 0.5 0.5 26.61 26.88 26.61
4 3.0 1.5 0.5 0.5 29.54 30.30 30.09
5 1.0 0.5 1.5 0.5 23.99 25.17 24.19
6 3.0 0.5 1.5 0.5 33.27 32.12 33.61
7 1.0 1.5 1.5 0.5 38.23 35.38 37.35
8 3.0 1.5 1.5 0.5 38.03 38.60 36.99
9 1.0 0.5 0.5 1.0 11.25 12.03 10.58
10 3.0 0.5 0.5 1.0 18.08 20.46 17.23
11 1.0 1.5 0.5 1.0 18.45 19.13 18.34
12 3.0 1.5 0.5 1.0 23.67 23.83 23.66
13 1.0 0.5 1.5 1.0 23.62 22.39 23.76
14 3.0 0.5 1.5 1.0 29.54 30.62 30.14
15 1.0 1.5 1.5 1.0 27.35 28.73 27.42
16 3.0 1.5 1.5 1.0 31.25 33.22 31.07
17 0.0 1.0 1.0 0.75 14.88 16.43 14.63
18 4.0 1.0 1.0 0.75 30.50 28.07 30.24
19 2.0 0.0 1.0 0.75 24.21 24.36 24.02
20 2.0 2.0 1.0 0.75 38.98 37.94 38.78
21 2.0 1.0 0.0 0.75 16.21 15.37 16.61
22 2.0 1.0 2.0 0.75 34.07 34.03 34.19
23 2.0 1.0 1.0 0.25 28.69 30.98 28.50
24 2.0 1.0 1.0 1.25 24.90 21.73 25.14
25 2.0 1.0 1.0 0.75 38.39 38.73 38.82
26 2.0 1.0 1.0 0.75 38.82 38.73 38.79
27 2.0 1.0 1.0 0.75 38.82 38.73 38.80
28 2.0 1.0 1.0 0.75 39.03 38.73 38.80
29 2.0 1.0 1.0 0.75 38.61 38.73 38.80
30 2.0 1.0 1.0 0.75 38.77 38.73 38.74
31 2.0 1.0 1.0 0.75 38.71 38.73 38.75

81
Table 4.5 Estimated regression coefficients for optimization of media
components using CCD
Variable
Estimated
coefficients
t-value
p-value
Constant 38.73 53.559

82
proline 2.18%, sodium nitrate 1.44%, L-asparagine 1.34% and glucose 0.63%
with maximum predicted L-asparaginase activity of 42.61 IU/mL (Table 4.7).
Table 4.6 Analysis of variance of RSM regression model relating
significant media components and L-asparaginase activity
Source
Degree of
freedom
(DF)
Sum of
squares
(SS)
Mean
square
(MS)
F-value
p-value
Regression 14 2111.14 150.796 41.18

83
4.4.1.2 Optimization of media components using artificial neural
network linked genetic algorithm
Back propagation algorithms for a multilayer feed-forward ANN
shown in Figure 4.27 was used to model the nonlinear relationships between
significant media components and L-asparaginase activity. This network was
used to train and evaluate the dependence of L-asparaginase activity on media
components using adaptive gradient learning rule with learning rate of 0.1 and
momentum of 0.4. ANN input parameters used for optimization of media
components for L-asparaginase production by A. terreus using MCD media
containing L-proline as substrate is shown Figure A.2.1. The ANN model
predicted the L-asparaginase activity with high coefficient of determination
(Cal.R 2 =0.997). This indicates that the ANN model is highly accurate in
successful prediction of L-asparaginase activity. Hence the ANN shown in
Figure 4.27 adequately represents the relationship between the media
components and L-asparaginase activity. Though both RSM regression model
and ANN model provided accurate predictions, ANN model (Cal.R 2 =0.997)
showed better correlation with the high experimental L-asparaginase activity
(40.86 IU/mL) than the RSM regression model (Cal.R 2 =0.973). Hence the
optimization of significant media components for L-asparaginase production
using the ANN model has provided accurate predictions and found to be more
effective with a high degree of accuracy than CCD of RSM.
Closeness of the experimental L-asparaginase activity and predicted
L-asparaginase activity using RSM and ANN model is shown in Figure 4.28.
The predicted R 2 -value of ANN model (Pred.R 2 =0.995) indicates that the
predicted L-asparaginase activity of ANN model was more close to the
experimental L-asparaginase activity than RSM model (Pred.R 2 =0.935). The
predicted R 2 was nearer to the calculated R 2 in RSM regression model than ANN
model. Hence the CCD optimization using RSM regression model was found to be
more efficient and accurate in predicting the optimal concentration of media
components for maximum L-asparaginase production than ANN model.

84
GA with population size of 30, mutation rate of 0.1 and uniform
cross overrate of 0.8 was used to optimize the ANN model to find the
maximum L-asparaginase activity and optimum concentration of media
components. The predicted optimum concentration of the medium
components was L-proline 1.7%, sodium nitrate 1.99%, L-asparagine 1.38%
and glucose 0.65%. The maximum predicted L-asparaginase activity of
39.95 IU/mL at the optimum concentration is reported in Table 4.7. The
percentage importance of significant medium components on L-asparaginase
production was evaluated using GA and it is given in Figure 4.29. The
percentage contribution of medium components on L-asparaginase production
was found to be 35.05% by glucose, 28.13% by L-proline, 24.11% by
L-asparagine and 12.71% by sodium nitrate. Best five optimum conditions
predicted by ANN linked GA for maximum L-asparaginase activity is given in
Table A.2.1.The predicted optimum concentration of the media components
by ANN model was found as L-proline 1.7%, sodium nitrate 1.99%,
L-asparagine 1.38% and glucose 0.65% with the validated experimental
L-asparaginase production of 39.95 IU/mL.
Figure 4.27 Back propagation artificial neural network for four input
layer with four hidden layer

85
Figure 4.28 Predicted distribution coefficient of RSM and ANN model
predicted L-asparaginase activity
Figure 4.29 Importance of media components on L-asparaginase
production (X 4 - Glucose; X 1 - L-proline; X 3 - L-asparagine;
X 2 - Sodium nitrate)

86
4.4.1.3 Experimental validation of RSM and ANN predicted conditions
The predicted condition obtained by RSM regression model and
ANN model were experimentally verified. The confirmation experiments
were conducted in triplicate using the optimum concentration of media
components of RSM regression model and ANN model. The concentration of
other media components and operations conditions were kept constant. The
experimental L-asparaginase activity obtained using predicted optimal
concentration of RSM regression model and ANN model are reported in
Table 4.7. The experimental L-asparaginase activity of 40.37 IU/mL was
obtained at the predicted optimal conditions of RSM regression model which
was 5.25% less than the predicted activity of 42.61 IU/mL. The experimental
L-asparaginase activity of 40.86 IU/mL was obtained at the predicted optimal
conditions of ANN linked GA which was 2.28% higher than the predicted
activity of 39.95 IU/mL. The ANN model was found to give better
experimental validation than RSM regression model. L-asparaginase activity
was found enhanced after ANN linked GA optimization approach when
compared to reported L-asparaginase activity by statistical and classical
method of one factor at a time approach. The reported activity for isolated
Bipolaris sp. BR438 is 6.20 IU/mL (Lapmak et al 2010), isolated Aspergillus
sp. 19.50 is IU/mL (Sreenivasulu et al 2009) and Streptomyces gulbargensis
is 23.90 IU/mL (Lingappa et al 2009). L-asparaginase activity was enhanced
by 14.37% than classical method of one factor at a time approach.
Table 4.7 Optimum concentration of media components, RSM and ANN
linked GA predicted and experimental L-asparaginase activity
Approach
Optimum
concentration, % (w/v)
L-Asparaginase activity,
IU/mL Cal. R 2 value
x 1 x 2 x 3 x 4 Predicted Experimental
RSM 2.18 1.44 1.34 0.63 42.61 40.37 0.973
ANN linked GA 1.70 1.99 1.38 0.65 39.95 40.86 0.997

87
4.4.2 Optimization of media components for L-asparaginase
production using SBMF as substrate
The independent effect of fermentation media components on
L-asparaginase production by A. terreus MTCC 1782 using natural substrate
SBMF in MCD media was evaluated using PB design. The first four
significant and important media components were further optimized using
RSM based 5-level CCD for four variables and ANN linked GA for
maximum L-asparaginase production.
4.4.2.1 Sequential optimization of media components using design of
experiments
The experimental L-asparaginase activity obtained using PBD
experiments are reported in Table 4.8; it showed a wide variation from 5.38 to
36.36 IU/mL. The independent effect, coefficients, t-value, p-value and
confidence level of the variables were estimated using MINITAB 15.0
software and reported in Table 4.9. The media components with positive
coefficients such as SBMF (x 1 ), sodium nitrate (x 2 ), L-asparagine (x 3 ),
glucose (x 4 ) and pH (x 8 ) have increased the L-asparaginase production. The
media components with negative coefficient such as di-potassium hydrogen
phosphate (x 5 ), magnesium sulfate (x 6 ) and potassium chloride (x 7 ) have
decreased the L-asparaginase production due to increase in the concentration.
Pareto plot in Figure 4.30 shows the relative importance of media components
and initial pH on L-asparaginase production. The media components with
confidence level of 90% and above were considered as significant and above
80–90% were considered as important for L-asparaginase production. It was
confirmed from Table 4.9 and Figure 4.30 that the L-asparagine and SBMF
are significant and sodium nitrate and glucose are other most important media
components for L-asparaginase production. The significant and important

88
media components were further optimized using CCD and ANN linked GA
for maximum L-asparaginase production.
The central composite design with two axial points at a distance
α=2 from the design center and six replicates about the center point making a
total of 31 run was used for optimization of media components. The student’s
t-test and Fisher’s F-test of ANOVA were performed using the experimental
L-asparaginase activity obtained using CCD given in Table 4.10. The
coefficients, t-value and p-value of t-test for linear, quadratic and combined
effects were given in the Table 4.11. The p-values are used as a tool to check
the significance of each variable at 95 % significance level, which in turn
indicates the pattern of the interaction effect of the variables on
L-asparaginase production. The smaller the p-value more significant is the
corresponding variable. It was observed from Table 4.11 that the overall
effect of the media components has significant (p

89
Table 4.8 PBD in actual unit of SBMF (x 1 ), sodium nitrate (x 2 ),
L-asparagine (x 3 ) and glucose (x 4 ), di-potassium hydrogen
phosphate (x 5 ), magnesium sulfate (x 6 ), potassium chloride
(x 7 ) and pH (x 8 ) with experimental L-asparaginase activity
Std.
order
Media components, % (w/v)
L-Asparaginase
activity, IU/mL
x 1 x 2 x 3 x 4 x 5 x 6 x 7 x 8
1 4.0 0.5 1.5 0.5 0.1 0.04 0.06 6.5 30.28
2 4.0 1.5 0.5 1.0 0.1 0.04 0.04 6.5 36.36
3 2.0 1.5 1.5 0.5 0.2 0.04 0.04 6.0 21.01
4 4.0 0.5 1.5 1.0 0.1 0.06 0.04 6.0 24.42
5 4.0 1.5 0.5 1.0 0.2 0.04 0.06 6.0 21.91
6 4.0 1.5 1.5 0.5 0.2 0.06 0.04 6.5 31.19
7 2.0 1.5 1.5 1.0 0.1 0.06 0.06 6.0 31.56
8 2.0 0.5 1.5 1.0 0.2 0.04 0.06 6.5 17.97
9 2.0 0.5 0.5 1.0 0.2 0.06 0.04 6.5 10.29
10 4.0 0.5 0.5 0.5 0.2 0.06 0.06 6.0 7.41
11 2.0 1.5 0.5 0.5 0.1 0.06 0.06 6.5 5.38
12 2.0 0.5 0.5 0.5 0.1 0.04 0.04 6.0 7.84
Table 4.9 Statistical analysis of PBD for evaluation of media components
and initial pH for L-asparaginase production
Variable
Main
effect
Coefficients t-value p-value
Intercept 20.472 10.71 0.002
Confidence
level, %
X 1 9.589 4.795 2.51 0.087 91.30
X 2 8.203 4.102 2.14 0.121 87.90
X 3 11.207 5.604 2.93 0.061 83.90
X 4 6.568 3.284 1.72 0.184 81.60
X 5 -4.346 -2.173 -1.14 0.338 76.20
X 6 -4.186 -2.093 -1.09 0.354 74.60
X 7 -2.764 -1.382 -0.72 0.522 47.80
X 8 2.888 1.444 0.76 0.505 49.50

90
Figure 4.30 Pareto plot of media components and operating conditions
for L-asparaginase activity (X 1 - SBMF; X 2 - sodium nitrate;
X 3 - L-asparagine; X 4 - glucose; X 5 - di-potassium hydrogen
phosphate; X 6 - magnesium sulfate; X 7 - potassium chloride;
X 8 - pH)
Table 4.10 Five-level CCD in actual unit of media components with
experimental, RSM and ANN predicted L-asparaginase activity
Std.
order
Media components, % (w/v)
x 1 x 2 x 3 x 4 Experimental
L-Asparaginase activity, IU/mL
RSM
predicted
ANN
predicted
1 2.0 1.0 0.5 0.5 16.42 17.01 16.13
2 4.0 1.0 0.5 0.5 23.84 24.06 24.01
3 2.0 2.0 0.5 0.5 16.69 16.74 16.69
4 4.0 2.0 0.5 0.5 19.30 22.73 19.43
5 2.0 1.0 1.5 0.5 36.42 33.60 36.38
6 4.0 1.0 1.5 0.5 32.26 35.03 32.46
7 2.0 2.0 1.5 0.5 23.67 23.77 23.58

91
Table 4.10 (Continued)
Std.
order
Media components, % (w/v)
x 1 x 2 x 3 x 4 Experimental
L-Asparaginase activity, IU/mL
RSM
predicted
ANN
predicted
8 4.0 2.0 1.5 0.5 26.39 24.13 26.27
9 2.0 1.0 0.5 1.0 12.32 11.38 14.78
10 4.0 1.0 0.5 1.0 7.84 9.74 7.67
11 2.0 2.0 0.5 1.0 14.18 13.41 14.17
12 4.0 2.0 0.5 1.0 11.09 10.71 10.86
13 2.0 1.0 1.5 1.0 30.71 29.29 30.84
14 4.0 1.0 1.5 1.0 25.27 22.02 25.13
15 2.0 2.0 1.5 1.0 25.16 21.74 25.19
16 4.0 2.0 1.5 1.0 11.99 13.41 12.20
17 1.0 1.5 1.0 0.75 15.94 19.66 16.01
18 5.0 1.5 1.0 0.75 20.90 18.38 20.88
19 3.0 0.5 1.0 0.75 18.66 19.54 18.54
20 3.0 2.5 1.0 0.75 10.34 10.67 10.31
21 3.0 1.5 0.0 0.75 16.10 13.45 13.72
22 3.0 1.5 2.0 0.75 28.90 32.75 28.68
23 3.0 1.5 1.0 0.25 34.77 33.14 37.37
24 3.0 1.5 1.0 1.25 13.97 16.80 14.13
25 3.0 1.5 1.0 0.75 37.38 37.28 37.05
26 3.0 1.5 1.0 0.75 37.22 37.28 37.13
27 3.0 1.5 1.0 0.75 37.06 37.28 36.85
28 3.0 1.5 1.0 0.75 37.70 37.28 36.88
29 3.0 1.5 1.0 0.75 36.85 37.28 36.86
30 3.0 1.5 1.0 0.75 37.27 37.28 36.85
31 3.0 1.5 1.0 0.75 37.48 37.28 37.07

92
Table 4.11 Estimated regression coefficients for optimization of media
components using CCD
Variable
Estimated
coefficients
t-value
p-value
Constant 37.281 36.241

93
L-asparaginase activity. Hence the RSM regression model given in Equation
4.2 was well fitted to represent the effect of media components on
L-asparaginase production using CCD.
Y
LA
= 37.28 - 0.32X - 2.22X
- 0.27X X
1
2
1
-1.41X X
1
3
2
- 2.17X X
+ 4.83X
1
4
3
- 4.08
- 2.39X X
2
4
3
- 4.56X
2
1
+ 0.57X X
2
- 5.54X
4
2
2
- 3.54X
+ 0.33X X
3
4
2
3
- 3.08X
2
4
(4.2)
where Y LA is L-asparaginase activity (IU/mL), X i and X j are media
components in coded units. The second-order regression model was solved
for maximum L-asparaginase production using MATLAB 7.0 program. The
optimum concentration of the media components was found as SBMF 3.01%,
sodium nitrate 1.29%, L-asparagine 1.39% and glucose 0.58% with the
predicted maximum L-asparaginase activity of 40.97 IU/mL.
Table 4.12
Analysis of variance of CCD optimization of media
components for L-asparaginase production
Source
Degree of
freedom
Sum of
squares
(SS)
Mean
square
(MS)
F-value
p-value
Regression 14 2919.80 208.55 28.15

94
2.5
2.0
1.5
Sodium nitrate, %*SBMF, %
10.3 10.3
4.2
22.5
16.4
34.7
2.0
1.5
1.0
L-asparaginae, %*SBMF, %
34.7
22.5
1.2
1.0
0.8
16.4
Glucose, %*SBMF, %
22.5
34.7
4.2
16.4
1.0
0.5
1.5
16.4
28.6
3.0
16.4
4.5
0.5
0.0
10.3
4.2
1.5
28.6
16.4
3.0
10.3
4.5
0.6
0.4
1.5
28.6
3.0
4.5
2.0
1.5
1.0
L-asparaginae, %*Sodium nitrate, %
34.7
10.3
22.5
1.2
1.0
0.8
Glucose, %*Sodium nitrate, %
10.3 22.5
4.2
16.4
34.7
1.2
1.0
0.8
Glucose, %*L-asparaginae, %
4.2
22.5
16.4
34.7
0.5
0.0
10.3
4.2
0.8
28.6
16.4
1.6
10.3
2.4
0.6
0.4
28.6
0.8
1.6
16.4
2.4
0.6
0.4
0
28.6
1
2
Figure 4.31 Interaction effects of variables on L-asparaginase activity
4.4.2.2 Optimization of media components using artificial neural
network linked genetic algorithm
The dependence of L-asparaginase activity on media components
was modeled using ANN shown in Figure 3.27 with ‘Tanh’ as transfer
function. The incremental back propagation algorithm was used to train and
evaluate the network performance using adaptive gradient learning rule with
learning rate of 0.1 and momentum of 0.4. The experimental L-asparaginase
activity given in Table 4.9 was used. Randomly selected 25 CCD
experimental data was used for training and 6 for testing the network. The
L-asparaginase activity was predicated using ANN trained model for all
experimental combination in CCD and reported in Table 4.9. The ANN model
has predicted the L-asparaginase activity with high coefficient of
determination (Cal.R 2 =0.996). Hence the ANN trained nonlinear model can
be effectively used to describe the effect of media components, namely,

95
SBMF, sodium nitrate, L-asparagine and glucose on L-asparaginase activity
and the back propagation algorithm of ANN was highly accurate in successful
prediction of L-asparaginase activity.
The predicted L-asparaginase activity of RSM regression model
was higher than the ANN model. ANN model (Cal.R 2 =0.996) showed better
correlation with the experimental L-asparaginase activity than RSM
regression model (Cal.R 2 =0.961). The closeness of the experimental
L-asparaginase activity and predicted L-asparaginase activity using RSM and
ANN model is shown in Figure 4.32. The pred.R 2 -value of ANN model
(Pred.R 2 =0.993) indicates that the predicted L-asparaginase activity of ANN
model was more close to the experimental L-asparaginase activity than RSM
model (Pred.R 2 =0.959).
The GA with population size of 30, uniform crossover rate of 0.8
and mutation rate of 0.1 was used to find the global optimum concentration of
the media components for maximum L-asparaginase activity using ANN
trained model. The degree of importance of the media components on
L-asparaginase production was analyzed using GA and it is given in
Figure 4.33. It was found that SBMF has contributed 33.28%, sodium nitrate
27.88%, L-asparagine 22.3% and glucose 16.56% on L-asparaginase
production. ANN linked GA predicted optimum concentration of media
components was SBMF 3.37%, sodium nitrate 1.17%, L-asparagine 1.36%
and glucose 0.25% with the maximum predicted L-asparaginase activity of
37.38 IU/mL (Table 4.13).

96
Figure 4.32 Predicted distribution coefficients for RSM and ANN
predicted L-asparaginase activity
Figure 4.33 Importance of media components on L-asparaginase activity
(X 1 - SBMF; X 2 - Sodium nitrate; X 3 - L-asparagine;
X 4 - Glucose)
4.4.2.3 Experimental validation of RSM and ANN predicted conditions
The predicted optimal concentration of media components obtained
using RSM regression model and ANN model were experimentally verified

97
by conducting experiments in triplicate. The concentration of other media
components and process conditions were kept constant as used in CCD
experiments. The experimental L-asparaginase activity of 40.76 IU/mL was
obtained at the predicted optimal conditions of RSM regression model and
38.95 IU/mL at the predicted optimal conditions of ANN linked GA
(Table 4.13). Although there was no significant difference in experimental
activity obtained in confirmation experiments of both RSM regression model
and ANN linked GA, RSM regression model of central composite design
gave better predicted and experimental results. Hence the CCD optimization
using RSM regression model was found to be more efficient and accurate in
predicting the optimal concentration of media components for maximum
L-asparaginase production. The experimental L-asparaginase activity obtained
at optimal conditions of RSM was 0.5% lower than the predicted activity of
40.97 IU/mL. L-asparaginase activity was enhanced by 46.72% than classical
method of one factor at a time approach. The L-asparaginase production was
enhanced after optimization when results obtained in classical methods and
compared to the reported L-asparaginase production using statistical and classical
methods (Lapmak et al 2010; Sreenivasulu et al 2009; Lingappa et al 2009).
Table 4.13 Optimum concentration of media components, RSM and
ANN predicted and experimental L-asparaginase activity
Approach
Optimum
concentration,
% (w/v)
L-Asparaginase
activity, IU/mL
x 1 x 2 x 3 x 4 Predicted Experimental
Cal.R 2
value
RSM 3.06 1.29 1.39 0.58 40.97 40.76 0.961
ANN linked GA 3.37 1.17 1.36 0.25 37.38 38.95 0.996

98
4.4.3 Optimization of media components for L-asparaginase
production using GNOC powder substrate
The independent effect of fermentation media components for
L-asparaginase production by A. terreus MTCC 1782 using MCD media
containing low cost natural substrate GNOC powder was evaluated using PB
design. The significant media components were further optimized for
maximum L-asparaginase production using RSM based 5-level CCD and
ANN linked GA.
4.4.3.1 Sequential optimization of media components using design of
experiments
L-Asparaginase activity obtained using PBD given Table 4.14 was
statistically analyzed. Media components with confidence level greater than
90% were considered to have significant influence on L-asparaginase
production. The estimated t-value, p-value and confidence level for
L-asparaginase activity are given in Table 4.15. On analysis of coefficients
and t-value of student’s t-test of the media components, GNOC (x 1 ), sodium
nitrate (x 2 ), L-asparagine (x 3 ) and sucrose (x 4 ) have increased (co-efficient
with positive sign) the L-asparaginase production due to increases in their
concentration, whereas di-potassium hydrogen phosphate (x 5 ), magnesium
sulfate (x 6 ), potassium chloride (x 7 ) and pH (x 8 ) have decreased (co-efficient
with negative sign) the L-asparaginase production due to increase in their
concentration. Sucrose with confidence level of 94.0%, GNOC with
confidence level of 93.4%, sodium nitrate with confidence level of 91.3% and
L-apsaragine with confidence level of 90.2% were found as significant
(p≤0.1) media components for L-asparaginase production. It also is confirmed
from Pareto plot in Figure 4.34 that the GNOC, sodium nitrate, L-asparagine
and sucrose are the significant media components on L-asparaginase
production and selected further optimization using RSM and ANN linked GA
for maximum L-asparaginase production.

99
Table 4.14 PBD in actual unit of GNOC (x 1 ), sodium nitrate (x 2 ),
L-asparagine (x 3 ) and sucrose (x 4 ), di-potassium hydrogen
phosphate (x 5 ), magnesium sulfate (x 6 ), potassium chloride
(x 7 ) and pH (x 8 ) with experimental L-asparaginase activity
Std.
order
Media components, % (w/v)
L-Asparaginase
activity, IU/mL
x 1 x 2 x 3 x 4 x 5 x 6 x 7 x 8
1 3 1 1.5 0.3 0.1 0.04 0.06 6.5 30.87
2 3 2 0.5 0.9 0.1 0.04 0.04 6.5 35.94
3 1 2 1.5 0.3 0.2 0.04 0.04 6.0 22.28
4 3 1 1.5 0.9 0.1 0.06 0.04 6.0 30.18
5 3 2 0.5 0.9 0.2 0.04 0.06 6.0 33.54
6 3 2 1.5 0.3 0.2 0.06 0.04 6.5 25.75
7 1 2 1.5 0.9 0.1 0.06 0.06 6.0 35.03
8 1 1 1.5 0.9 0.2 0.04 0.06 6.5 18.56
9 1 1 0.5 0.9 0.2 0.06 0.04 6.5 14.50
10 3 1 0.5 0.3 0.2 0.06 0.06 6.0 10.45
11 1 2 0.5 0.3 0.1 0.06 0.06 6.5 11.30
12 1 1 0.5 0.3 0.1 0.04 0.04 6.0 15.62
Table 4.15
Statistical analysis of PBD for evaluation of media
components and initial pH on L-asparaginase production
Variable
Main
effect
Coefficients t-value p-value
Intercept 23.668 16.32 0.001
Confidence
level, %
X 1 8.240 4.120 2.84 0.066 93.4
X 2 7.277 3.638 2.51 0.087 91.3
X 3 6.887 3.443 2.37 0.098 90.2
X 4 8.580 4.290 2.96 0.060 94.0
X 5 -5.643 -2.822 -1.95 0.147 85.3
X 6 -4.933 -2.467 -1.70 0.188 81.2
X 7 -0.753 -0.377 -0.26 0.812 18.8
X 8 -1.697 -0.848 -0.58 0.600 40.0

100
Figure 4.34 Pareto plot shows the effect of media components and
operating conditions on L-asparaginase activity (X 1 - GNOC;
X 2 - Sodium nitrate; X 3 -L-asparagine; X 4 - Sucrose; X 5 - Dipotassium
hydrogen phosphate; X 6 - Magnesium sulfate;
X 7 - Potassium chloride; X 8 - initial pH)
The central composite design of 31 experimental run was used for
the optimization of significant fermentation media components like GNOC
(x 1 ), sodium nitrate (x 2 ), L-asparagine (x 3 ) and sucrose (x 4 ). The concentration
of other media components was kept at constant. The student’s t-test and
F-test were performed using MINITAB 15.0 software for experimental activity
given in Table 4.16. The coefficients, t-value and p-value for linear, quadratic
and combined effects are given in the Table 4.17, at 95% significance level.
The overall effect of the media components on L-asparaginase production was
highly significant (p

101
significantly (p=0.004) decreased the L-asparaginase production. There was
no significant change in L-asparaginase production due the interaction effect
of the media components GNOC versus sodium nitrate (p=0.607), GNOC
versus L-asparagine (p=0.154), GNOC versus sucrose (p=0.599), sodium
nitrate versus sucrose (p=0.630) and L-asparagine versus sucrose (p=0.203) at
95% confidence level (p>0.05).
The interaction effect between any two variable was also studied
graphically using contour plots as shown in Figure 4.35, other two variables
were kept constant at their middle level. The elliptical shape of the contour
plot for sodium nitrate versus L-asparagine indicates the significant interaction
effect on L-asparaginase production. The contour plot of GNOC versus
L-asparagine was not perfectly circular, which indicates the interaction effect
and decreases the L-asparaginase production (co-efficient with negative sign
in Table 4.17). The circular shape of the contour plots among the other
variables illustrated in Figure 4.35 indicates that there was less or no
interaction.
Table 4.16 Five-level CCD in actual unit of media components with
experimental, RSM and ANN predicted L-asparaginase activity
Std.
order
Media components, % (w/v)
x 1 x 2 x 3 x 4 Experimental
L-Asparaginase activity, IU/mL
RSM
predicted
ANN
predicted
1 1.0 1.0 0.5 0.3 21.70 21.31 21.70
2 3.0 1.0 0.5 0.3 24.10 21.34 24.05
3 1.0 2.0 0.5 0.3 33.43 29.79 33.44
4 3.0 2.0 0.5 0.3 29.43 28.58 29.00
5 1.0 1.0 1.5 0.3 29.17 26.83 29.78
6 3.0 1.0 1.5 0.3 29.17 30.40 29.27

102
Table 4.16 (Continued)
Std.
order
Media components, % (w/v)
x 1 x 2 x 3 x 4 Experimental
L-Asparaginase activity, IU/mL
RSM
predicted
ANN
predicted
7 1.0 2.0 1.5 0.3 26.29 27.44 27.53
8 3.0 2.0 1.5 0.3 30.77 29.76 30.18
9 1.0 1.0 0.5 0.9 22.72 20.93 22.05
10 3.0 1.0 0.5 0.9 21.38 19.70 21.51
11 1.0 2.0 0.5 0.9 30.02 28.26 29.99
12 3.0 2.0 0.5 0.9 26.23 25.78 26.20
13 1.0 1.0 1.5 0.9 29.27 29.59 29.51
14 3.0 1.0 1.5 0.9 31.03 31.89 31.15
15 1.0 2.0 1.5 0.9 29.06 29.04 28.16
16 3.0 2.0 1.5 0.9 30.23 30.09 30.29
17 0.0 1.5 1.0 0.6 27.78 30.35 27.77
18 4.0 1.5 1.0 0.6 30.71 31.45 30.48
19 2.0 0.5 1.0 0.6 27.03 28.65 27.11
20 2.0 2.5 1.0 0.6 33.64 35.34 34.23
21 2.0 1.5 0.0 0.6 16.21 21.21 16.55
22 2.0 1.5 2.0 0.6 32.74 31.05 32.41
23 2.0 1.5 1.0 0.0 16.05 18.69 16.45
24 2.0 1.5 1.0 1.2 17.97 18.64 18.30
25 2.0 1.5 1.0 0.6 35.46 35.33 35.59
26 2.0 1.5 1.0 0.6 35.08 35.33 35.46
27 2.0 1.5 1.0 0.6 35.56 35.33 35.44
28 2.0 1.5 1.0 0.6 35.46 35.33 35.66
29 2.0 1.5 1.0 0.6 35.14 35.33 35.42
30 2.0 1.5 1.0 0.6 35.35 35.33 35.57
31 2.0 1.5 1.0 0.6 35.25 35.33 35.65

103
Table 4.17
Estimated regression coefficients for optimization of
significant media components using CCD
Variable
Estimated
coefficients
t-value
p-value
Constant 35.331 39.564

104
to represent the effect of the variables on L-asparaginase production
using CCD.
Y
LA
= 35.33 + 0.27X
- 2.3X
2
3
- 0.29X
2
X
4
1
- 4.17X
+ 1.67X
2
4
+ 0.78X
3
X
2
- 0.31X X
4
+ 2.46X
1
2
3
- 0.01X
+ 0.88X X
1
3
4
-1.11X
1
2
1
- 0.32X X
- 0.83X
4
2
2
-1.97X
2
X
3
(4.3)
where Y LA is the L-asparaginase activity (IU/mL), X i and X j are independent
variables in coded units. The regression model was solved for maximum
L-asparaginase production using MATLAB 7.0 program. GNOC 2.18%,
sodium nitrate 1.69%, L-asparagine 1.21% and sucrose 0.62% were obtained
as optimum concentration of significant media components with the
maximum predicted L-asparaginase activity of 36.21 IU/mL.
Table 4.18 Analysis of variance of RSM regression model relating
significant media components and L-asparaginase activity
Source
Degree of
freedom
Sum of
squares
(SS)
Mean
square
(MS)
F-value
p-value
Regression 14 906.850 64.775 11.60

105
2.5
2.0
1.5
Sodium nitrate, %*GNOC, %
33
2.0
1.5
1.0
L-asparagine, %*GNOC, %
33
29
1.00
0.75
0.50
Sucrose, %*GNOC, %
25 21
33
29
1.0
0.5
0
29
2
4
0.5
0.0
0
29 25
2
21
4
0.25
0.00
0
25
21
2
29
4
2.0
1.5
1.0
L-asparagine, %*Sodium nitrate, %
33 29
33
1.00
0.75
0.50
Sucrose, %*Sodium nitrate, %
21
25
33
1.00
0.75
0.50
Sucrose, %*L-asparagine, %
9 17
25
21
33
0.5
0.0
25
17
0.8
21
1.6
29
2.4
0.25
0.00
25
17
0.8
21
1.6
29
2.4
0.25
0.00
0
17
29
1
21
2
Figure 4.35 Interaction effects of variables on L-asparaginase
production in contour plots
4.4.3.2 Optimization by artificial neural network linked genetic algorithm
A multilayer feed-forward ANN using back propagation algorithm
was used to model the nonlinear relationships between media components
(GNOC, sodium nitrate, L-asparagine and sucrose) and L-asparaginase
activity obtained using CCD. The ANN with four neurons in input layer, four
in hidden layer and one in output layer shown in Figure 4.27 was used with
‘Tanh’ as transfer function. The high coefficient of determination
(Cal.R 2 =0.995) of ANN model indicates that the model is highly accurate in
successful prediction of L-asparaginase activity. Hence the ANN can be
effectively used to represent the relationship between the media components
studied and L-asparaginase activity. The ANN predicted L-asparaginase
activity is given in Table 4.16. High value of Cal.R 2 =0.995 of ANN model
showed better correlation with the experimental L-asparaginase activity than
RSM regression model (Cal.R 2 =0.91).

106
Closeness of the experimental L-asparaginase activity and predicted
L-asparaginase activity using RSM and ANN model is shown in Figure 4.36.
The pred.R 2 -value of ANN model (Pred.R 2 =0.994) indicates that the predicted
L-asparaginase activity of ANN model was more close to the experimental
L-asparaginase activity than RSM model (Pred.R 2 =0.901).
ANN model was solved using GA with population size of 30,
mutation rate of 0.1 and uniform crossover rate of 0.8 to find the optimum
concentration of the media components for the maximum L-asparaginase
production. The percentage importance of the media components was found
to be sucrose, 35.05%; L-asparagine, 24.77%; sodium nitrate, 24.42%;
GNOC, 19.38% on L-asparaginase production (Figure 4.37). The predicted
optimum concentration of the media components were GNOC, 3.99%;
sodium nitrate, 1.04%; L-asparagine, 1.84% and sucrose, 0.64% with
maximum predicted L-asparaginase production of 36.64 IU/mL (Table 4.19).
Though both RSM regression model and ANN model provided accurate
predictions, ANN model (R 2 =0.995) showed better correlation with the
experimental L-asparaginase activity than RSM regression model (R 2 =0.91).
The predicted conditions obtained by RSM regression model and ANN model
were experimentally verified.

107
Figure 4.36 Predicted distribution coefficient of RSM and ANN model
predicted L-asparaginase activity
Figure 4.37 Importance of media components on L-asparaginase
production (X 1 - GNOC; X 2 - Sodium nitrate;
X 3 - L-asparagine; X 4 - Sucrose)

108
4.4.3.3 Experimental validation of RSM and ANN predicted conditions
The confirmation experiments were conducted in triplicate using
predicted optimum concentration of media components by both RSM
regression model and ANN model for validation. All the other fermentation
conditions were fixed as CCD experiment for optimization. The experimental
L-asparaginase activity of 35.56 IU/mL is obtained using predicted optimal
concentrations of RSM regression model (Table 4.19). The experimental
L-asparaginase activity of 36.97 IU/mL was obtained using the predicted
optimal concentration of ANN model which was nearer to the ANN predicted
activity than RSM regression model. The experimental activity was higher in
ANN optimum conditions than RSM optimum conditions, which indicates
that the ANN model was more accurate. The L-asparaginase activity was
enhanced and it is higher than the reported activity for isolated Aspergillus sp.
19.50 IU/mL (Sreenivasulu et al 2009), isolated Bipolaris sp. (BR438)
6.20 IU/mL (Lapmak et al 2010) and Streptomyces gulbargensis 23.90 IU/mL
(Lingappa et al 2009).
Table 4.19 Optimum concentration of media components, RSM and
ANN predicted and experimental L-asparaginase activity
Approach
Optimum
L-Asparaginase activity,
concentration,
IU/mL
% (w/v)
x 1 x 2 x 3 x 4 Predicted Experimental
Cal.R 2
value
RSM 2.18 1.69 1.21 0.62 36.21 35.56 0.910
ANN linked GA 3.99 1.04 1.84 0.64 36.64 36.97 0.995

109
4.5 STATISTICAL AND EVOLUTIONARY OPTIMIZATION
OF PROCESS CONDITIONS FOR ENHANCED
L-ASPARAGINASE PRODUCTION BY A. terreus MTCC 1782
Fermentation process conditions such as temperature (x 1 ), initial pH
(x 2 ), inoculum size (x 3 ), agitation rate (x 4 ) and fermentation time (x 5 ) were
optimized using RSM based CCD and ANN linked GA for L-asparaginase
production by submerged fermentation of A. terreus MTCC 1782 using
different substrate, namely, SBMF, GNOC and synthetic L-proline as
substrate in MCD media.
4.5.1 Optimization of process conditions for L-asparaginase
production using synthetic L-proline as substrate
RSM based 3-level CCD for five variables and ANN linked GA
were used for optimization of process conditions for enhanced production of
L-asparaginase by A. terreus MTCC 1782 using synthetic L-proline as
substrate.
4.5.1.1 Optimization of process conditions using response surface
methodology
Experimental L-asparaginase activity obtained using CCD in Table
4.20 was statistically analyzed by student’s t-test and Fisher’s F-test at 95%
confidence level (p=0.05). The coefficients, t-value and p-value for linear,
quadratic and interaction effect of process conditions are given in the
Table 4.21. The overall effect of process conditions on L-asparaginase
production was found highly significant (p

110
and interaction effect of all the process conditions were found to have no
significant effect (p

111
Table 4.20 (Continued)
Run
order
X 1 ,
°C X 2
Process conditions
X 3 ,
% (v/v)
X 4 ,
rpm
L-Asparaginase activity, IU/mL
X 5 ,
h Experimental RSM
predicted
ANN
predicted
15 28 5.8 1 120 96 16.26 16.25 16.42
16 28 5.8 3 180 96 21.86 21.31 21.86
17 33 6.3 2 180 72 39.67 39.39 39.64
18 33 6.3 2 150 72 43.51 40.35 43.54
19 33 6.3 2 150 48 38.50 40.43 38.35
20 28 6.3 2 150 72 27.99 31.43 28.04
21 33 6.3 1 150 72 42.50 40.07 42.37
22 33 5.8 2 150 72 30.55 31.29 30.37
23 33 6.3 2 150 72 42.71 40.35 43.57
24 38 5.8 3 120 96 17.97 17.07 14.74
25 38 5.8 1 180 96 18.87 19.79 19.04
26 38 6.8 1 180 48 22.55 23.17 22.55
27 38 5.8 1 120 48 25.06 25.43 25.01
28 38 6.8 3 180 96 17.01 16.37 17.23
29 33 6.3 2 150 72 42.97 40.35 43.54
30 33 6.3 2 150 72 43.99 40.35 43.52
31 33 6.3 2 150 72 43.08 40.35 43.53
32 38 5.8 3 180 48 26.13 25.95 26.13

112
Table 4.21 Estimated regression coefficients of RSM regression model
for optimization of process conditions
Variable Estimated coefficients t-value p-value
Constant 40.358 34.762

113
Figure 4.38 Interaction effects of process conditions on L-asparaginase
production in contour plots (X 1 - Temperature, °C;
X 2 - initial pH; X 3 - Inoculum size, % (v/v); X 4 - Agitation
rate, rpm; X 5 - Incubation time, h)
The ANOVA results of the RSM regression model is given in
Table 4.22, at 95% confidence level. The ANOVA results demonstrate that
the model is highly significant (p

114
Y
LA
=
40.36 + 1.57X
-1.06X
-1.03X
2
3
2
3
1
- 0.89X
X
- 0.39X
2
4
-1.21X
2
X
4
2
-1.14X
- 0.78X
2
5
+ 1.03X
2
3
- 0.36X X
X
- 0.07X
5
1
2
4
-1.23X
+ 0.82X
-1.21X
3
X
4
1
X
3
5
- 0.31X
- 7.35X
3
X
5
2
1
- 0.64X X
1
- 9.45X
4
- 0.43X
4
2
2
- 0.57X
X
5
1
X
(4.4)
5
where Y LA is the L-asparaginase activity (IU/mL), X i and X j are independent
variables in coded units. Optimum process conditions were found by solving
RSM regression model in Equation (4.4) for maximum L-asparaginase
production. Temperature 33.85°C, initial pH 6.29, inoculum size 1.52% (v/v),
agitation rate 144 rpm and incubation time of 60.61 h were found to be
optimum conditions with maximum predicted L-asparaginase activity of 40.97
IU/mL.
Table 4.22 Analysis of variance of RSM for optimization of process
conditions
Source
Degree of
freedom
Sum of
squares (SS)
Mean square
(MS)
F-value p-value
Regression 20 2815.03 140.751 8.53

115
conditions for L-asparaginase production by A. terreus MTCC 1782 using
synthetic L-proline. ‘Tanh’ was used as neuron activation function. The
experimental L-asparaginase activity given in Table 4.20 was used to train the
ANN to understand the dependence of L-asparaginase production on operating
conditions such as temperature, pH, inoculum size, agitation rate and
incubation time using neural power software. A multilayer feed-forward ANN
using incremental back propagation algorithm and adaptive gradient learning
rule with learning rate of 0.1 and momentum of 0.4 was used to train the
ANN using randomly selected 25 experimental run in Table 4.20 and
performance was tested using other 7 experimental run.
The trained ANN was used to predict the L-asparaginase activity
for all the CCD data set on operating conditions given in Table 4.20. The high
coefficient of determination (Cal.R 2 =0.999) for L-asparaginase activity
predicted by ANN model indicates that the ANN is highly accurate in
successful prediction of L-asparaginase activity. Hence the ANN shown in
Figure 4.39 can be used to develop the relationship between the operating
conditions and L-asparaginase activity. ANN predicted L-asparaginase activity
is given in Table 4.20. Though both RSM regression model and ANN model
provided accurate predictions, ANN showed better and accurate correlation
with the experimental L-asparaginase activity than RSM regression model.
The closeness of the experimental L-asparaginase activity with RSM and
ANN predicted L-asparaginase activity is shown in Figure 4.40. The higher
predicted R 2 value of 0.995 for the ANN model than RSM model
(Pred.R 2 =0.935) indicates the high degree of accuracy of the ANN model.
Hence Figure 4.41 shows a good agreement between experimental and ANN
predicted L-asparaginase activity.
GA with population size of 30, mutation rate of 0.1 and uniform
crossover rate of 0.8 was run for 94501 iterations to find the predicted

116
optimum operating conditions for maximum L-asparaginase activity. The
percentage importance of the operating conditions on L-asparaginase
production was found to be: temperature 25.28%, initial pH 24.81%, agitation
rate 18.87%, incubation time 18.1% and inoculum size 13.15%, as shown in
Figure 4.41. It was predicted that the temperature of 35°C, pH of 6.25,
inoculum size of 1%, agitation rate of 140.18 rpm and incubation time of
58.45 h favours the maximum production of L-asparaginase. The maximum
predicted L-asparaginase activity at ANN predicted process condition was
44.38 IU/mL (Table 4.23).
4.5.1.3 Experimental validation of RSM and ANN predicted conditions
Confirmation experiments were conducted in triplicate at predicted
optimum process conditions of RSM and ANN models for validation. All
other fermentation conditions and medium composition were fixed as in CCD
experiments. The experimental L-asparaginase activity of 40.56 IU/mL was
obtained at the predicted optimal conditions of RSM regression model
(Table 4.23). The experimental L-asparaginase activity of 43.29 IU/mL was
obtained at the predicted optimal conditions of ANN linked GA, which is
close to the maximum predicted L-asparaginase activity of 44.38 IU/mL.
L-asparaginase production was enhanced by 23.58% higher than classical
method of one factor at a time approach after optimization using evolutionary
method, and it is higher than RSM method and other reports in literature
(Sreenivasulu et al 2009; Lingappa et al 2009; Lapmak et al 2010). The
optimization process conditions for L-asparaginase production using
L-proline as substrate by ANN linked GA was found to be more efficient and
accurate than RSM.

117
Figure 4.39 Back propagation neural network with five input layers and
four hidden layers used for optimization of process
conditions for L-asparaginase production
Figure 4.40 Predicted distribution coefficients of RSM and ANN
predicted L-asparaginase activity

118
Figure 4.41 Importance of operating conditions on L-asparaginase
production (X 1 -Temperature, °C; X 2 - initial pH;
X 3 - Inoculum size, % (v/v); X 4 - Agitation rate, rpm;
X 5 - Incubation time, h)
Table 4.23 Optimum process conditions with experimental, RSM and
ANN predicted L-asparaginase activity
Method
Optimum process
conditions
X 1 ,°C X 2 X 3 ,% X 4,
rpm
L-asparaginase activity,
IU/mL Cal.R 2
X 5, h Predicted Experimental
value
RSM 33.85 6.29 1.52 144.24 60.61 40.97 40.56 0.939
ANN linked
GA
35.06 6.25 1.00 140.18 58.45 44.38 43.29 0.999
4.5.2 Optimization of process conditions for L-asparaginase
production using soybean meal flour as substrate
The RSM based 3-level CCD for five variables and ANN linked
GA were used for optimization of the process conditions for optimization of
fermentation culture conditions for L-asparaginase production by A. terreus
MTCC 1782 utilizing SBMF as natural substrate in MCD media.

119
4.5.2.1 Optimization of culture conditions using response surface
methodology
The student’s t-test and F-test were performed using MINITAB 15.0
software for experimental L-asparaginase activity obtained using CCD
reported in Table 4.24. The coefficients, t-value and p-value for linear,
quadratic and combined effects were given in the Table 4.25, at 95%
significance level. The L-asparaginase production was significantly (p

120
plots among the variables indicates that there was no or less interaction effect
by the variables on L-asparaginase activity.
The effect of the culture conditions on L-asparaginase activity was
fitted into the second-order polynomial model given in Equation 4.5 using
regression analysis. Statistical analysis of the regression model in the form of
analysis of variance (ANOVA), which is required to test the significance and
adequacy of the model, is reported in Table 4.26, at 95% confidence level.
The ANOVA of the regression model demonstrates that the model is highly
significant (p

121
4.5.2.2 Optimization of process conditions using artificial neural
network linked genetic algorithm
The ANN with five neurons in input layer, four in hidden layer and
one in output layer shown in Figure 4.39 was used for optimization of process
conditions for L-asparaginase production by A. terreus MTCC 1782 using
SBMF. ‘Tanh’ was used as neuron activation function. The experimental
L-asparaginase activity given in Table 4.24 was used to train the ANN to
understand the dependence of L-asparaginase production on operating
conditions such as temperature, pH, inoculum size, agitation rate and
incubation time using neural power software. A multilayer feed-forward ANN
using incremental back propagation algorithm and adaptive gradient learning
rule with learning rate of 0.1 and momentum of 0.4 was used to train the
ANN using randomly selected 25 experimental run in Table 4.24 and
performance was tested using other 7 experimental run.
Table 4.24 Three-level CCD in actual unit of temperature (x 1 ), initial
pH (x 2 ), inoculum size (x 3 ), agitation rate (x 4 ) and
fermentation time (x 5 ) with experimental, RSM and ANN
predicted L-Asparaginase activity
Run
order
X 1 ,
°C X 2
Process conditions
X 3 ,
% (v/v)
X 4 ,
rpm
L-Asparaginase activity, IU/mL
X 5 ,
h Experimental RSM
predicted
ANN
predicted
1 33 6.3 2 150 48 34.76 36.01 35.16
2 38 5.8 1 120 48 22.28 22.26 22.21
3 33 6.3 3 150 72 28.36 30.56 28.41
4 38 5.8 1 180 96 17.49 17.87 17.44
5 33 6.3 2 150 72 40.15 37.84 39.91
6 28 5.8 1 120 96 25.64 25.29 25.70
7 33 6.3 2 150 72 40.31 37.84 39.91

122
Table 4.24 (Continued)
Run
order
X 1 ,
°C X 2
Process conditions
X 3 ,
% (v/v)
X 4 ,
rpm
L-Asparaginase activity, IU/mL
X 5 ,
h Experimental RSM
predicted
ANN
predicted
8 38 6.8 3 120 48 16.95 16.39 17.02
9 33 6.3 2 180 72 36.36 36.02 35.59
10 33 6.8 2 150 72 32.10 35.34 32.21
11 33 6.3 2 150 96 32.47 34.49 32.99
12 33 5.8 2 150 72 35.62 35.65 35.73
13 28 6.3 2 150 72 27.51 30.09 27.59
14 38 5.8 3 120 96 17.38 17.12 16.74
15 33 6.3 2 150 72 39.88 37.84 39.91
16 33 6.3 2 120 72 33.16 36.77 32.74
17 38 6.8 3 180 96 14.82 14.66 14.97
18 28 5.8 3 180 96 18.77 18.77 18.79
19 38 5.8 3 180 48 17.49 17.82 17.91
20 33 6.3 2 150 72 40.20 37.84 39.91
21 33 6.3 2 150 72 39.94 37.84 39.91
22 38 6.8 1 120 96 24.79 24.27 24.98
23 28 6.8 3 120 96 13.11 12.22 12.70
24 28 5.8 1 180 48 24.15 24.39 24.11
25 33 6.3 1 150 72 37.48 38.55 37.69
26 38 6.3 2 150 72 28.58 29.28 28.65
27 28 6.8 1 180 96 24.10 23.84 24.05
28 28 6.8 1 120 48 28.36 27.70 28.37
29 38 6.8 1 180 48 26.44 26.51 26.23
30 28 6.8 3 180 48 13.59 13.29 13.63
31 28 5.8 3 120 48 18.29 17.89 18.24
32 33 6.3 2 150 72 39.72 37.84 39.91

123
Table 4.25 Estimated regression coefficients of RSM regression model
for optimization of process conditions
Variable
Estimated
coefficients
t-value
p-value
Constant 37.845 51.006

124
Table 4.26 Analysis of variance of RSM for optimization of process
conditions
Source
Degree of
freedom
Sum of
squares
(SS)
Mean
square
(MS)
F-value
p-value
Regression 20 2441.06 122.053 18.11

125
The trained ANN was used to predict the L-asparaginase activity
for all the CCD data set on operating conditions in Table 4.24. The high
coefficient of determination (Cal.R 2 =0.999) for L-asparaginase activity
predicted by ANN model indicates that the ANN was highly accurate in
successful prediction of L-asparaginase activity. Hence the ANN shown in
Figure 4.39 can be effectively used to develop the relationship between the
operating conditions and L-asparaginase activity. ANN predicted
L-asparaginase activity is given in Table 4.24 Though both RSM regression
model and ANN model provided accurate predictions, ANN showed better
and accurate correlation with experimental L-asparaginase activity than RSM
regression model. The closeness of the experimental L-asparaginase activity
with RSM and ANN predicted L-asparaginase activity is shown in Figure
4.43. The higher predicted R 2 value for ANN model (Pred.R 2 =0.995) than
RSM model (Pred.R 2 =0.969) and closer value of predicted (Pred.R 2 =0.970)
and calculated coefficient of determination (Cal.R 2 =0.999) indicate the high
degree of accuracy of ANN model for optimization of culture conditions.
Hence Figure 4.43 shows a good agreement between experimental and ANN
predicted L-asparaginase activity.
GA with population size of 30, mutation rate of 0.1 and uniform
crossover rate of 0.8 was run for 35501 iterations to find the predicted
optimum operating conditions for maximum L-asparaginase activity. The
percentage importance of the operating conditions on L-asparaginase
production was found to be: temperature 24.79%, initial pH 20.87%, agitation
rate 18.98%, incubation time 17.8% and inoculum size 17.55% (Figure 4.44).
It was predicted that the temperature of 32.13°C, initial pH of 5.8, inoculum
size of 1%, agitation rate of 179.98 rpm and incubation time of 63.03 h favors
the maximum production of L-asparaginase. The maximum predicted
L-asparaginase activity at ANN predicted process condition was 40.86 IU/mL
(Table 4.27).

126
Figure 4.43 Predicted distribution coefficients of RSM and ANN
predicted L-asparaginase activity
Figure 4.44 Importance of operating conditions on L-asparaginase
production (X 1 - temperature, °C; X 2 - initial pH;
X 3 - Inoculum size, % (v/v); X 4 - Agitation rate, rpm;
X 5 - Incubation time, h)

127
4.5.2.3 Experimental validation of RSM and ANN predicted conditions
Confirmation experiments were conducted in triplicate at predicted
optimum process conditions of RSM and ANN models for validation. All
other fermentation conditions and medium composition were fixed as in CCD
experiments. The experimental L-asparaginase activity of 40.85 IU/mL was
obtained at the predicted optimum process conditions of RSM regression
model (Table 4.27) and it is closer to the predicted activity (39.37 IU/mL).
The experimental L-asparaginase activity of 41.78 IU/mL was obtained at the
predicted optimum conditions of ANN linked GA, which is close to the
maximum predicted L-asparaginase activity of 40.86 IU/mL. The
experimental L-asparaginase activity obtained using ANN linked GA was
higher than RSM predicted and experimental activity. Thus optimization
process conditions for L-asparaginase production using SBMF as substrate by
ANN linked GA was found to be more efficient and accurate than RSM. The
L-asparaginase production was enhanced by 23.58% higher than classical
method of one-factor at a time approach after optimization using evolutionary
method and it is higher than other reports found in literature (Sreenivasulu
et al 2009; Lingappa et al 2009; Lapmak et al 2010).
Table 4.27 Optimum process conditions with experimental, RSM and
Method
ANN predicted L-asparaginase activity
Optimum process
conditions
L-Asparaginase
activity,
IU/mL
Cal.R 2
value
X 1 ,°C X 2 X 3 ,% X 4,
rpm
X 5, h Predicted Experimental
RSM 32.74 6.41 1.28 143.03 66.91 39.37 40.85 0.970
ANN
32.13 5.80 1.00 179.98 63.03 40.86 41.78 0.999
linked GA

128
4.5.3 Optimization of process conditions for L-asparaginase
production using groundnut oil cake as substrate
The RSM based 3-level CCD for five variables and ANN linked
GA were used for optimization of process conditions for enhanced production
of L-asparaginase by submerged fermentation A. terreus MTCC 1782 utilizing
GNOC powder as low cost natural substrate in MCD media.
4.5.3.1 Optimization of operating conditions using response surface
methodology
The student’s t-test and F-test of RSM regression model were
performed for the experimental L-asparaginase activity obtained using CCD
given in Table 4.28. The coefficients, t-value and p-value for linear, quadratic
and combined effects were given in the Table 4.28, at 95% significance level.
It was observed that the overall effect of all process conditions studied have
significantly increased (p

129
were kept constant at their middle level. The circular or near circular shape of
the contour plots among the process conditions indicate that there was less or
no interaction effect on L-asparaginase production.
The result of ANOVA of RSM regression model in Equation (4.6)
is given in Table 4.30, at 95% confidence level. The ANOVA of the
regression model demonstrates that the model is highly significant (p

130
4.5.3.2 Optimization using artificial neural network linked genetic
algorithm
A multilayer feed-forward ANN shown in Figure 4.40 was used for
optimization of process conditions for
L-asparaginase production by
A. terreus MTCC 1782 using GNOC powder. Incremental back propagation
algorithm was used to train and test the performance of the network using
adaptive gradient learning rule. The neuron activation function ‘Tanh’ was
used with learning rate of 0.1 and momentum of 0.4. The network was trained
to understand the dependence of L-asparaginase activity on operating
conditions using randomly selected 25 experimental run in Table 4.30 and
performance was tested using other 7 experimental run.
Table 4.28 Three-level CCD in actual unit of temperature (x 1 ), initial
pH (x 2 ), inoculum size (x 3 ), agitation rate (x 4 ) and
fermentation time (x 5 ) with experimental, RSM and ANN
predicted L-asparaginase activity
Run
order
X 1 ,
°C X 2
Process conditions
X 3 ,
% (v/v)
X 4 ,
Rpm
L-Asparaginase activity, IU/mL
X 5 ,
h Experimental RSM
predicted
ANN
predicted
1 38 5.8 1 120 48 20.21 19.92 19.87
2 28 6.8 1 180 96 24.36 24.06 24.38
3 33 5.8 2 150 72 31.51 34.51 31.54
4 38 6.3 2 150 72 19.83 22.02 19.91
5 28 6.8 3 180 48 14.82 14.72 14.77
6 28 6.8 1 120 48 18.98 19.03 19.1
7 28 5.8 1 180 48 24.10 23.63 24.34
8 33 6.3 2 150 72 37.22 34.59 36.88
9 38 5.8 1 180 96 19.51 18.87 19.38

131
Table 4.28 (Continued)
Run
order
X 1 ,
°C X 2
Process conditions
X 3 ,
% (v/v)
X 4 ,
Rpm
L-Asparaginase activity, IU/mL
X 5 ,
h Experimental RSM
predicted
ANN
predicted
10 33 6.3 2 150 72 37.11 34.59 36.88
11 28 5.8 3 120 48 20.63 20.50 20.64
12 38 5.8 3 120 96 16.31 16.02 16.82
13 38 5.8 3 180 48 16.79 16.35 16.68
14 38 6.8 1 120 96 10.82 10.70 10.87
15 38 6.8 3 180 96 15.57 15.30 15.31
16 33 6.3 2 150 72 37.16 34.59 36.88
17 38 6.8 1 180 48 15.14 14.88 15.41
18 28 6.8 3 120 96 15.67 15.71 15.64
19 33 6.8 2 150 72 29.27 30.12 29.09
20 33 6.3 2 150 96 31.30 33.64 31.19
21 38 6.8 3 120 48 11.78 11.86 11.56
22 33 6.3 1 150 72 31.88 34.19 32.53
23 33 6.3 2 120 72 32.15 33.09 32.03
24 33 6.3 2 150 48 31.40 32.92 31.05
25 33 6.3 2 150 72 37.22 34.59 36.88
26 33 6.3 2 150 72 37.27 34.59 36.88
27 33 6.3 2 150 72 37.06 34.59 36.88
28 28 5.8 1 120 96 25.91 25.58 25.79
29 33 6.3 3 150 72 29.43 30.97 29.90
30 28 5.8 3 180 96 20.95 20.47 20.62
31 33 6.3 2 180 72 31.30 34.21 31.52
32 28 6.3 2 150 72 25.32 26.99 25.45

132
Table 4.29 Estimated regression coefficients of RSM regression model
for optimization of process conditions
Variable Estimated coefficients t-value p-value
Constant 34.595 43.841

133
Table 4.30 Analysis of variance of RSM for optimization of process
conditions
Source
Degree of
freedom
(DF)
Sum of
squares
(SS)
Mean
square
(MS)
F-value
p-value
Regression 20 2151.64 107.582 14.11

134
The coefficient of determination (Cal.R 2 =0.999) of ANN model
indicates that the ANN was highly accurate in successful prediction of
L-asparaginase activity. The ANN shown in Figure 4.39 can be effectively
used to develop the relationship between the process conditions and
L-asparaginase activity. ANN predicted L-asparaginase activity given in
Table 4.31 was obtained after 9618 iterations. Though both RSM and ANN
models provided accurate predictions, ANN model (Cal.R 2 =0.999) showed
better correlation with the experimental L-asparaginase activity than RSM
regression model (Cal.R 2 =0.962). Closeness of the experimental
L-asparaginase activity with RSM and ANN predicted L-asparaginase activity
is shown in Figure 4.46. The higher predicted coefficient of determination
(Pred.R 2 =0.999) of the ANN model than RSM model (Pred.R 2 =0.961)
indicates the high degree of accuracy of the ANN model. Hence Figure 4.46
shows good agreement between experimental and predicted L-asparaginase
activity.
Once the ANN model was developed and tested, GA was applied to
optimize the model for maximum L-asparaginase activity. The GA with
population size of 30, uniform crossover rate of 0.8 and mutation rate of 0.1
was run for 71001 iterations to find the global optimum values of the process
conditions for maximum L-asparaginase activity. The percentage importance
of process conditions on L-asparaginase production was studied using GA. It
was found to be maximum by agitation rate 28.99% followed by temperature
23.17%, initial pH 17.47%, fermentation time 15.91% and inoculum size
14.46% on L-asparaginase production as shown in Figure 4.47. Temperature
of 32.08°C, initial pH of 5.8, inoculum size of 1% (v/v), agitation rate of
123.5 rpm and fermentation time of 55.1 h was found to give maximum
L-asparaginase activity of 38.57 IU/mL (Table 4.31).

135
Figure 4.46 Predicted distribution coefficients of RSM and ANN
predicted L-asparaginase activity
Figure 4.47 Importance of operating conditions on L-asparaginase
production (X 1 - Temperature, °C; X 2 - initial pH;
X 3 - Inoculum size, % (v/v); X 4 - Agitation rate, rpm;
X 5 - Incubation time, h)

136
4.5.3.3 Experimental validation of RSM and ANN predicted conditions
Confirmation experiments were conducted in triplicate at the
predicted optimum process conditions of RSM and ANN linked GA for
validation. All other fermentation conditions and medium composition were
fixed as used in CCD experiments for optimization. The experimental
L-asparaginase activity of 34.58 IU/mL (Table 4.31) was obtained at the
predicted optimum conditions of RSM model. The experimental
L-asparaginase activity obtained at the predicted optimum conditions of ANN
linked GA was of 37.84 IU/mL which is very nearer to the ANN predicted
maximum activity of 38.57 IU/mL. Also, the predicted and experimental
L-asparaginase activity obtained using ANN linked GA were higher than
RSM approach. Hence the optimization process condition for L-asparaginase
production using GNOC by ANN linked GA was found to be more efficient
and accurate than RSM. The L-asparaginase activity was found enhanced
through optimization of operating conditions and it is higher than other
findings reported in literature (Sreenivasulu et al 2009; Lingappa et al 2009;
Lapmak et al 2010). The L-asparaginase production was enhanced by 7.19%
higher than classical method of one factor at a time approach after
optimization using ANN linked GA.
Table 4.31 Optimum process conditions with experimental, RSM and
ANN predicted L-asparaginase activity
Method
L-Asparaginase
Optimum conditions
activity, IU/mL Cal. R 2
value
X 1 ,°C X 2 X 3 ,% X 4,
rpm
X 5, h Predicted Experimental
RSM 32.24 6.07 1.52 155.15 75.63 35.73 34.58 0.962
ANN
linked
GA
32.08 5.80 1.00 123.50 55.10 38.57 37.84 0.999

137
4.6 BENCH SCALE PRODUCTION AND UNSTRUCTURED
KINETIC MODELING OF L-ASPARAGINASE
PRODUCTION BY A. terreus MTCC 1782
A 5 L bench scale bioreactor (3 L working volume) was used for
batch production of L-asparaginase by submerged fermentation of A. terreus
MTCC 1782 using two different substrates, namely, synthetic L-proline and
SBMF. The Monod (M), Logistic (T) and Modified Logistic (ML) models
discussed in Chapter 3.14.1 were studied to fit experimental biomass to
understand the growth kinetics of A. terreus for L-asparaginase production,
and growth kinetic parameters such as μ 0 , μ m , K s and “r” were estimated. The
Monod incorporated Leudeking–Piret model (MLP), Logistic incorporated
Leudeking–Piret model (LLP) and Modified Logistic incorporated
Leudeking–Piret model (MLLP) discussed in Chapter 3.14.2 were studied to
fit experimental L-asparaginase activity, and model parameters such as α
(growth associated product formation parameter) and (non-growth
associated product formation parameter) were estimated to understand the
L-asparaginase production kinetics and its dependence on growth of by
A. terreus. The Monod incorporated Modified Leudeking–Piret model
(MMLP), Logistic incorporated Modified Leudeking–Piret model (LMLP)
and Modified Logistic incorporated Modified Leudeking–Piret model
(MLMLP) discussed in Chapter 3.14.3 were studied to fit experimental
residual glucose concentration and model parameters such (growth
associated substrate utilization parameter) and η (non-growth associated
substrate utilization parameter) were estimated to understand the substrate
utilization kinetics and its influence on fungal growth and product formation
rate. The biomass concentration, L-asparaginase activity and residual glucose
concentration were predicted using the kinetic parameters of the respective
models. The estimated kinetic parameters and model predicted values of
biomass concentration, L-asparaginase activity and residual glucose

138
concentration were statistically analyzed and compared with experimental
values to find the best model. The dependence of L-asparaginase production
on growth of A. terreus and glucose utilization rate were discussed.
4.6.1 Kinetics modeling of L-asparaginase production using synthetic
L-proline
The MCD media (3 L) containing synthetic L-proline was used for
L-asparaginase production by A. terreus MTCC 1782 in a 5 L bench
bioreactor. The biomass concentration, L-asparaginase activity and residual
glucose concentration were measured periodically up 72 h with an interval of
8 h. The profile of L-asparaginase activity, biomass and residual glucose
concentration in time course of fermentation are given in Figure 4.48. The
L-asparaginase production started to increase significantly after the eighth
hour of fermentation when the growth of the microorganism reaches the midexponential
phase. The maximum L-asparaginase activity of 44.58 IU/mL was
obtained in post-exponential growth phase at 64 h and remains constant after
72 h. The rate of L-asparaginase formation and growth were high in
exponential phase and low in post-exponential phase and constant in
stationary phase. The glucose utilization rate was low in early growth phase
until 8 h and high in exponential phase until 48 h of fermentation and it was
low in late exponential phase (after 56 h to 64 h) and was very low in
stationary phase (after 64 h). Almost 94% of glucose was depleted in 56 h of
fermentation. The exponential phase of the fungal growth was observed from
8 to 48 h. The biomass concentration has reached a maximum of 5.29 g/L in
64 h and there was no further increase in biomass concentration until 72 h.
The biomass concentration has decreased slowly. L-asparaginase production
was noted in exponential growth phase, post-exponential growth phase as
well as in early stationary phase.

139
The kinetic data on L-asparaginase production shown in Figure 4.48
were tested to fit various kinetic models discussed in Chapter 3.14 on growth,
product formation and substrate utilization rate. The closeness of model
predicted and experimental values were statically analyzed using the concept
of coefficient of determination (R 2 ), average percentage error (δ m ) and chisquare
test (χ 2 ) to find the best-suited model. The goodness of fit of the
various models was compared using δ m and χ 2 statistic analysis. The summary
of estimated kinetic parameters and statistical analysis of various models are
given in Table 4.32. The tabulated χ 2 value at 95% confidence level for
9 degrees of freedom is 16.02. The R 2 value of various models studied on
growth kinetics of A. terreus for L-asparaginase production is shown in
Figure 4.49. Sample MATLAB output is shown in Figure A3.1. The
coherence of experimental and ML model predicted biomass concentration
was high with high R 2 value (0.989). δ m (1.91%) and χ 2 (0.13) values were
also low for ML model when compared to all other models studied. This
implies that the ML model is most appropriate to best describe the microbial
growth of A. terreus for L-asparaginase production. Hence the growth kinetics
of A. terreus for L-asparaginase production follows exponential growth and it
is under catabolic repression with high substrate inhibition coefficient
(r=0.985) indicates low glucose inhibition.
The R 2 value of various models studied on L-asparaginase
formation kinetics by A. terreus is shown in Figure 4.50. The coherence of
experimental and MLLP model predicted the L-asparaginase activity was high
with high R 2 value (0.977). δ m (12.62%) and χ 2 (3.21) values were also
minimum for MLLP model when compared to all other models. This implies
that the MLLP model is most appropriate to describe the L-asparaginase
formation kinetics by A. terreus. The magnitude of growth-associated
parameter (α=4.216) is much greater than the non-growth associated

140
parameter (=0.083) in MLLP model which is typical for growth-associated
product formation. Hence the L-asparaginase production by A. terreus is
dependent on biomass concentration and increases with increase in fungal
growth. This also reveals that the L-asparaginase production is under catabolic
repression with high substrate inhibition coefficient (r = 0.985) indicating low
glucose inhibition.
The R 2 value of the various models studied on glucose utilization
kinetics for L-asparaginase production by A. terreus is shown in Figure 4.51.
The coherence of the experimental and MLMLP model predicted residual
glucose concentration was high with high R 2 value (0.918). δ m (9.71%) and χ 2
(3.46) values were also minimum for MLMLP model when compared to all
other models. This implies that the MLMLP is the most appropriate model to
describe the glucose utilization kinetics by A. terreus for L-asparaginase
production. The magnitude of growth-associated parameter (=1.104) is much
greater than magnitude of the non-growth associated parameter (η=0.024) in
MLMLP model which is typical for growth-associated substrate utilization.
Hence the fermentation profile of glucose utilization is growth associated with
high substrate inhibition coefficient (r=0.985). The statistical analysis of the
experimental data showed that the unstructured models could satisfactorily
explain the batch kinetics of L-asparaginase production. The closeness of
experimental and best fitted model predicted biomass concentration,
L-asparaginase activity and residual glucose concentration is shown in
Figure 4.52.

141
Figure 4.48 Profile of microbial growth, L-asparaginase formation and
glucose utilization rate for L-asparaginase production by
A. terreus (○Biomass, ∆Residual glucose, *L-asparaginase
activity)
Figure 4.49 Correlation coefficient of various models studied for growth
kinetics of A. terreus MTCC 1782 for L-asparaginase
production (□M, ∆L, *ML)

142
Figure 4.50 Correlation coefficient of various models studied for
L-asparaginase formation kinetics by A. terreus MTCC 1782
(□MLP; ×LLP; ○MLLP)
Figure 4.51 Correlation coefficient of various models studied for glucose
utilization kinetics by A. terreus MTCC 1782 for
L-asparaginase production (◊MMLP; ×LMLP; ○MLMLP)

143
Figure 4.52 Profile of experimental (symbols) and model predicted
(hatched lines) biomass concentration using ML (Δ),
L-asparaginase activity using MLLP (○) and residual
glucose concentration using MLMLP (□)
Table 4.32
Estimated kinetic parameters and statistical analysis of various
unstructured kinetic models for fungal growth, L-asparaginase
formation and glucose utilization rate for L-asparaginase
production by A. terreus using synthetic L-proline
Product formation
Growth kinetics
Constant
kinetics
Glucose utilization kinetics
M L ML MLP LLP MLLP MMLP LMLP MLMLP
μ 0, h -1 - 0.136 0.121 - 0.136 0.121 - 0.136 0.121
μ m, h -1 0.071 - - 0.071 - - 0.071 - -
K s, g/L 0.859 - - 0.859 - - 0.859 - -
R - - 0.985 - - 0.985 - - 0.985
X 0 g/L - 0.23 0.16 - 0.23 0.16 - 0.23 0.16
X m , g/L - 5.31 5.31 - 5.31 5.31 - 5.31 5.31
- - - 1.536 3.505 4.216 - - -
- - - 0.081 0.067 0.083 - - -
Γ - - - - - - 0.483 0.936 1.104
Η - - - - - - 0.005 0.022 0.024
R 2 0.692 0.960 0.989 0.634 0.954 0.977 0.101 0.906 0.918
δ m -52.47 8.86 1.91 31.06 -0.45 12.62 372.56 10.84 9.71
χ 2 28.53 0.54 0.13 88.85 8.40 3.21 6.92 4.47 3.46

144
4.6.2 Kinetics modeling of L-asparaginase production using SBMF
The MCD media (3 L) containing SBMF of was used for
L-asparaginase production by A. terreus MTCC 1782 in a 5 L bench
bioreactor. The biomass concentration, L-asparaginase activity and residual
glucose concentration were measured periodically upto 72 h with an interval
of 8 h. The experimental biomass concentration, L-asparaginase activity and
residual glucose concentration is shown in Figure 4.53. The exponential phase
of the fungal growth was observed from 8 h to 40 h. The biomass
concentration reaches a maximum of 5.73g/L at 56 h and there is no further
increase in biomass concentration. The L-asparaginase production starts to
increase significantly after the eighth hour of fermentation when the growth
of the microorganism reaches the mid-exponential phase. The L-asparaginase
production was noted in the growth phase of the A. terreus and maximum at
56 h of fermentation. The maximum L-asparaginase activity of 38.87 IU/mL
was observed in exponential growth phase at 56 h. The L-asparaginase
production rate and growth rate were high in exponential phase and low in
post-exponential phase. The glucose utilization rate was low in early growth
phase upto 8 h, high in exponential phase from 8 to 48 h, low in late
exponential phase from 48 to 64 h and was very low in stationary phase from
64 h onwards. The rate of glucose utilization by the microorganism increases
rapidly up to 56 h and then declines when microbial growth reaches the
stationary phase. Almost 92.81% of glucose was depleted within 56 h.
Various unstructured kinetic models discussed in chapter 3.14 were
mathematically analyzed to study the characteristics of L-asparaginase
production by A. terreus. The growth kinetic parameters such as μ 0 , μ m , K s ,
and ‘r’ of the models were estimated. The summary of growth kinetic
parameters of the various models is given in Table 4.33. The estimated kinetic
parameters were used to simulate the models for predicted biomass

145
concentration using MATLAB 7 program. The predicted biomass
concentration of models compared with experimental values as shown in
Figure 4.54. The R 2 of experimental and predicted values were analyzed to
find the best suited model. The goodness of fit of the various models was also
compared using χ 2 statistic analysis and average mean error (δ m ).
Determination coefficient, average percentage error and χ 2 values between
experimental and predicted biomass concentration are reported in Table 4.33.
The tabulated χ 2 value at 95% confidence level for degree of freedom of 9 is
16.02. The coherence of the predicted and experimental biomass
concentration (R 2 =0.997) in the Logistic model implies that this model is
most appropriate to describe the growth kinetics of A. terreus for
L-asparaginase production. The χ 2 (0.02

146
model was found to be the best model to represent L-asparaginase formation
by A. terreus 1782. All other model predictions have shown larger deviation
from experimental L-asparaginase activity. The magnitude of the growthassociated
parameter (α=4.656) is much greater than the magnitude of the
non-growth associated parameter (=0.119) for LLP model. Hence, the
fermentation production of L-asparaginase by A. terreus is a typical growthassociated
process.
The MMLP model, LMLP and MLMLP models were fit to
represent the experimental residual glucose concentration. The estimated
kinetic parameters reported in Table 4.33 were used to simulate the models
for predicted residual glucose concentration using MATLAB 7 program. The
predicted residual glucose concentration of models was compared with
experimental values as shown in Figure 4.56. The simulation results of the
LMLP model is in good agreement with the experimental L-asparaginase
activity, implies that the LMLP model (R 2 =0.995) is the most appropriate to
represent the glucose utilization kinetics in L-asparaginase production by
A. terreus. The χ 2 (1.55

147
Figure 4.53 Profile of microbial growth, L-asparaginase formation and
glucose utilization rate for L-asparaginase production by
A. terreus (○Biomass, ∆Residual glucose, *L-asparaginase
activity)
Figure 4.54 Correlation coefficient of various models studied on growth
kinetics of A. terreus (□M, *L, ○ML)

148
Figure 4.55 Correlation coefficient of various models studied on
L-asparaginase formation kinetics (□MLP, *LLP, ○MLLP)
Figure 4.56 Correlation coefficient of various models studied glucose
utilization kinetics (□MMLP, *LMLP, ○MLMLP)

149
Figure 4.57 Experimental (symbols) and model predicted (hatched lines)
biomass concentration using Logistic model (Δ),
L-asparaginase activity using LLP (○) and residual glucose
concentration using LMLP (□)
Table 4.33 Estimated kinetic parameters and statistical analysis of
various unstructured kinetic models for fungal growth,
L-asparaginase formation and glucose utilization rate for
L-asparaginase production by A. terreus using SBMF
Constant
Growth kinetics Product formation kinetics Glucose utilization kinetics
M L ML MLP LLP MLLP MMLP LMLP MLMLP
μ 0, h -1 - 0.116 0.081 - 0.116 0.081 - 0.116 0.081
μ m, h -1 0.069 - - 0.069 - - 0.069 - -
K s, g/L 2.351 - - 2.351 - - 2.351 - -
R - - 0.921 - - 0.921 - - 0.921
X 0 g/L - 0.370 0.370 - 0.370 0.370 - 0.370 0.370
X m , g/L - 5.790 5.790 - 5.790 5.790 - 5.790 5.790
- - - 3.706 4.656 9.776 - - -
- - - 0.249 0.119 0.251 - - -
Γ - - - - - - 1.186 0.551 1.075
Η - - - - - - 0.086 0.013 0.021
R 2 0.773 0.997 0.973 0.858 0.993 0.968 0.979 0.995 0.640
δ m 37.24 0.21 44.73 51.42 18.68 34.56 827.33 12.34 57.05
χ 2 4.91 0.02 4.84 212.64 3.88 43.24 50.47 1.55 3.05